{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\L03547535\Dropbox\Tec de Monterrey\US Experiment (Prolific)\US Experiment (UNT)\JEPS Replication Files\JEPS_log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 3 May 2025, 04:30:19
{txt}
{com}. 
. clear
{txt}
{com}. use JEPS_Data.dta
{txt}
{com}. 
. ***Vairable Codings***
. **Experimental Condition
. gen condition=.
{txt}(1,515 missing values generated)

{com}. replace condition=1 if support_g1!=""
{txt}(379 real changes made)

{com}. replace condition=2 if support_g2!=""
{txt}(375 real changes made)

{com}. replace condition=3 if support_g3!=""
{txt}(382 real changes made)

{com}. replace condition=4 if support_g4!=""
{txt}(379 real changes made)

{com}. label define condition 1 "China/No Commitment" 2 "China/Commitment" 3 "Japan/No Commitmnet" 4 "Japan/Commitment"
{txt}
{com}. label values condition condition
{txt}
{com}. tab condition

          {txt}condition {c |}      Freq.     Percent        Cum.
{hline 20}{c +}{hline 35}
China/No Commitment {c |}{res}        379       25.02       25.02
{txt}   China/Commitment {c |}{res}        375       24.75       49.77
{txt}Japan/No Commitmnet {c |}{res}        382       25.21       74.98
{txt}   Japan/Commitment {c |}{res}        379       25.02      100.00
{txt}{hline 20}{c +}{hline 35}
              Total {c |}{res}      1,515      100.00
{txt}
{com}. 
. **Demographics
. *Gender
. tab gender

              {txt}Gender {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
              Female {c |}{res}        738       48.71       48.71
{txt}                Male {c |}{res}        756       49.90       98.61
{txt}              Others {c |}{res}         19        1.25       99.87
{txt}Prefer not to answer {c |}{res}          2        0.13      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}      1,515      100.00
{txt}
{com}. g Male=.
{txt}(1,515 missing values generated)

{com}. replace Male=1 if gender=="Male"
{txt}(756 real changes made)

{com}. replace Male=0 if gender=="Female"
{txt}(738 real changes made)

{com}. tab Male

       {txt}Male {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        738       49.40       49.40
{txt}          1 {c |}{res}        756       50.60      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,494      100.00
{txt}
{com}. 
. *Age
. tab age

        {txt}Age {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         19 {c |}{res}          1        0.07        0.07
{txt}         20 {c |}{res}          6        0.40        0.46
{txt}         21 {c |}{res}         13        0.86        1.32
{txt}         22 {c |}{res}         17        1.12        2.44
{txt}         23 {c |}{res}         25        1.65        4.09
{txt}         24 {c |}{res}         30        1.98        6.07
{txt}         25 {c |}{res}         39        2.57        8.65
{txt}         26 {c |}{res}         36        2.38       11.02
{txt}         27 {c |}{res}         34        2.24       13.27
{txt}         28 {c |}{res}         36        2.38       15.64
{txt}         29 {c |}{res}         50        3.30       18.94
{txt}         30 {c |}{res}         55        3.63       22.57
{txt}         31 {c |}{res}         37        2.44       25.02
{txt}         32 {c |}{res}         43        2.84       27.85
{txt}         33 {c |}{res}         60        3.96       31.82
{txt}         34 {c |}{res}         54        3.56       35.38
{txt}         35 {c |}{res}         53        3.50       38.88
{txt}         36 {c |}{res}         43        2.84       41.72
{txt}         37 {c |}{res}         60        3.96       45.68
{txt}         38 {c |}{res}         43        2.84       48.51
{txt}         39 {c |}{res}         47        3.10       51.62
{txt}         40 {c |}{res}         41        2.71       54.32
{txt}         41 {c |}{res}         38        2.51       56.83
{txt}         42 {c |}{res}         34        2.24       59.08
{txt}         43 {c |}{res}         37        2.44       61.52
{txt}         44 {c |}{res}         22        1.45       62.97
{txt}         45 {c |}{res}         39        2.57       65.54
{txt}         46 {c |}{res}         26        1.72       67.26
{txt}         47 {c |}{res}         31        2.05       69.31
{txt}         48 {c |}{res}         37        2.44       71.75
{txt}         49 {c |}{res}         29        1.91       73.66
{txt}         50 {c |}{res}         30        1.98       75.64
{txt}         51 {c |}{res}         47        3.10       78.75
{txt}         52 {c |}{res}         21        1.39       80.13
{txt}         53 {c |}{res}         23        1.52       81.65
{txt}         54 {c |}{res}         24        1.58       83.23
{txt}         55 {c |}{res}         15        0.99       84.22
{txt}         56 {c |}{res}         18        1.19       85.41
{txt}         57 {c |}{res}         27        1.78       87.19
{txt}         58 {c |}{res}         19        1.25       88.45
{txt}         59 {c |}{res}         16        1.06       89.50
{txt}         60 {c |}{res}         17        1.12       90.63
{txt}         61 {c |}{res}         14        0.92       91.55
{txt}         62 {c |}{res}         19        1.25       92.81
{txt}         63 {c |}{res}         11        0.73       93.53
{txt}         64 {c |}{res}          7        0.46       93.99
{txt}         65 {c |}{res}         11        0.73       94.72
{txt}         66 {c |}{res}         11        0.73       95.45
{txt}         67 {c |}{res}          8        0.53       95.97
{txt}         68 {c |}{res}          8        0.53       96.50
{txt}         69 {c |}{res}         13        0.86       97.36
{txt}         70 {c |}{res}          6        0.40       97.76
{txt}         71 {c |}{res}          8        0.53       98.28
{txt}         72 {c |}{res}          4        0.26       98.55
{txt}         73 {c |}{res}          4        0.26       98.81
{txt}         74 {c |}{res}          2        0.13       98.94
{txt}         75 {c |}{res}          3        0.20       99.14
{txt}         76 {c |}{res}          6        0.40       99.54
{txt}         77 {c |}{res}          3        0.20       99.74
{txt}         78 {c |}{res}          1        0.07       99.80
{txt}         79 {c |}{res}          1        0.07       99.87
{txt}         80 {c |}{res}          1        0.07       99.93
{txt}         83 {c |}{res}          1        0.07      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,515      100.00
{txt}
{com}. g Age=.
{txt}(1,515 missing values generated)

{com}. replace Age=1 if age>=18 & age<=24
{txt}(92 real changes made)

{com}. replace Age=2 if age>=25 & age<=34
{txt}(444 real changes made)

{com}. replace Age=3 if age>=35 & age<=44
{txt}(418 real changes made)

{com}. replace Age=4 if age>=45 & age<=54
{txt}(307 real changes made)

{com}. replace Age=5 if age>=55 & age<=64
{txt}(163 real changes made)

{com}. replace Age=6 if age>=65
{txt}(91 real changes made)

{com}. tab Age

        {txt}Age {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         92        6.07        6.07
{txt}          2 {c |}{res}        444       29.31       35.38
{txt}          3 {c |}{res}        418       27.59       62.97
{txt}          4 {c |}{res}        307       20.26       83.23
{txt}          5 {c |}{res}        163       10.76       93.99
{txt}          6 {c |}{res}         91        6.01      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,515      100.00
{txt}
{com}. 
. label define Age 1 "18-24" 2 "25-34" 3 "35-44" 4 "45-54" 5 "55-64" 6 "65 or Over"
{txt}
{com}. label values Age Age
{txt}
{com}. 
. *Race
. tab race

                {txt}Race {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
               Asian {c |}{res}         96        6.34        6.34
{txt}               Black {c |}{res}        210       13.86       20.20
{txt}               Mixed {c |}{res}        104        6.86       27.06
{txt}               Other {c |}{res}         67        4.42       31.49
{txt}Prefer not to answer {c |}{res}          8        0.53       32.01
{txt}               White {c |}{res}      1,030       67.99      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}      1,515      100.00
{txt}
{com}. g White=.
{txt}(1,515 missing values generated)

{com}. replace White=1 if race=="White"
{txt}(1,030 real changes made)

{com}. replace White=0 if race=="Asian" | race=="Black" | race=="Mixed" | race=="Other" 
{txt}(477 real changes made)

{com}. sum White

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}White {c |}{res}      1,507    .6834771    .4652739          0          1
{txt}
{com}. label define White 1 "White" 0 "Non Whites"
{txt}
{com}. label values White White
{txt}
{com}. tab White

      {txt}White {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
 Non Whites {c |}{res}        477       31.65       31.65
{txt}      White {c |}{res}      1,030       68.35      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,507      100.00
{txt}
{com}. 
. *Ideology(Respondent)
. tab ideology

                    {txt}Ideology {c |}      Freq.     Percent        Cum.
{hline 29}{c +}{hline 35}
                Conservative {c |}{res}        251       16.57       16.57
{txt}                  Don't Know {c |}{res}          3        0.20       16.77
{txt}      Extremely Conservative {c |}{res}         94        6.20       22.97
{txt}           Extremely Liberal {c |}{res}        182       12.01       34.98
{txt}                     Liberal {c |}{res}        304       20.07       55.05
{txt}Moderate, Middle of the Road {c |}{res}        307       20.26       75.31
{txt}        Prefer not to answer {c |}{res}          3        0.20       75.51
{txt}       Slightly Conservative {c |}{res}        185       12.21       87.72
{txt}            Slightly Liberal {c |}{res}        186       12.28      100.00
{txt}{hline 29}{c +}{hline 35}
                       Total {c |}{res}      1,515      100.00
{txt}
{com}. g Ideology=.
{txt}(1,515 missing values generated)

{com}. replace Ideology=1 if ideolog=="Extremely Conservative"
{txt}(94 real changes made)

{com}. replace Ideology=2 if ideolog=="Conservative"
{txt}(251 real changes made)

{com}. replace Ideology=3 if ideolog=="Slightly Conservative"
{txt}(185 real changes made)

{com}. replace Ideology=4 if ideolog=="Moderate, Middle of the Road"
{txt}(307 real changes made)

{com}. replace Ideology=5 if ideolog=="Slightly Liberal"
{txt}(186 real changes made)

{com}. replace Ideology=6 if ideolog=="Liberal"
{txt}(304 real changes made)

{com}. replace Ideology=7 if ideolog=="Extremely Liberal"
{txt}(182 real changes made)

{com}. tab Ideology

   {txt}Ideology {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         94        6.23        6.23
{txt}          2 {c |}{res}        251       16.63       22.86
{txt}          3 {c |}{res}        185       12.26       35.12
{txt}          4 {c |}{res}        307       20.34       55.47
{txt}          5 {c |}{res}        186       12.33       67.79
{txt}          6 {c |}{res}        304       20.15       87.94
{txt}          7 {c |}{res}        182       12.06      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,509      100.00
{txt}
{com}. sum Ideology

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}Ideology {c |}{res}      1,509    4.245858    1.817938          1          7
{txt}
{com}. 
. label define Ideology 1 "Extremely Conservative" 2 "Conservative" 3 "Slightly Conservative" 4 "Moderate, Middle of the Road/Don't Know" 5 "Slightly Liberal" 6 "Liberal" 7 "Extremely Liberal"
{txt}
{com}. label values Ideology Ideology
{txt}
{com}. tab Ideology

                               {txt}Ideology {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                 Extremely Conservative {c |}{res}         94        6.23        6.23
{txt}                           Conservative {c |}{res}        251       16.63       22.86
{txt}                  Slightly Conservative {c |}{res}        185       12.26       35.12
{txt}Moderate, Middle of the Road/Don't Know {c |}{res}        307       20.34       55.47
{txt}                       Slightly Liberal {c |}{res}        186       12.33       67.79
{txt}                                Liberal {c |}{res}        304       20.15       87.94
{txt}                      Extremely Liberal {c |}{res}        182       12.06      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      1,509      100.00
{txt}
{com}. 
. *Income
. tab income

              {txt}Income {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
 $100,000 - $124,999 {c |}{res}        191       12.61       12.61
{txt} $125,000 - $149,999 {c |}{res}        136        8.98       21.58
{txt} $150,000 - $174,999 {c |}{res}         82        5.41       27.00
{txt} $175,000 - $199,999 {c |}{res}         43        2.84       29.83
{txt}   $25,000 - $49,999 {c |}{res}        260       17.16       47.00
{txt}   $50,000 - $74,999 {c |}{res}        297       19.60       66.60
{txt}   $75,000 - $99,999 {c |}{res}        245       16.17       82.77
{txt}          Don't know {c |}{res}          8        0.53       83.30
{txt}   Less than $25,000 {c |}{res}        140        9.24       92.54
{txt}  More than $200,000 {c |}{res}         96        6.34       98.88
{txt}Prefer not to answer {c |}{res}         17        1.12      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}      1,515      100.00
{txt}
{com}. g Income=.
{txt}(1,515 missing values generated)

{com}. replace Income=1 if income=="Less than $25,000"
{txt}(140 real changes made)

{com}. replace Income=2 if income=="$25,000 - $49,999"
{txt}(260 real changes made)

{com}. replace Income=3 if income=="$50,000 - $74,999"
{txt}(297 real changes made)

{com}. replace Income=4 if income=="$75,000 - $99,999"
{txt}(245 real changes made)

{com}. replace Income=5 if income=="$100,000 - $124,999"
{txt}(191 real changes made)

{com}. replace Income=6 if income=="$125,000 - $149,999"
{txt}(136 real changes made)

{com}. replace Income=7 if income=="$150,000 - $174,999"
{txt}(82 real changes made)

{com}. replace Income=8 if income=="$175,000 - $199,999"
{txt}(43 real changes made)

{com}. replace Income=9 if income=="More than $200,000"
{txt}(96 real changes made)

{com}. tab Income

     {txt}Income {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        140        9.40        9.40
{txt}          2 {c |}{res}        260       17.45       26.85
{txt}          3 {c |}{res}        297       19.93       46.78
{txt}          4 {c |}{res}        245       16.44       63.22
{txt}          5 {c |}{res}        191       12.82       76.04
{txt}          6 {c |}{res}        136        9.13       85.17
{txt}          7 {c |}{res}         82        5.50       90.67
{txt}          8 {c |}{res}         43        2.89       93.56
{txt}          9 {c |}{res}         96        6.44      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,490      100.00
{txt}
{com}. 
. label define Income 1 "Less than $25,000" 2 "$25,000 - $49,999" 3 "$50,000 - $74,999" 4 "$75,000 - $99,999" 5 "$100,000 - $124,999" 6 "$125,000 - $149,999" 7 "$150,000 - $174,999" 8 "$175,000 - $199,999" 9 "More than $200,000"
{txt}
{com}. label values Income Income
{txt}
{com}. tab Income

             {txt}Income {c |}      Freq.     Percent        Cum.
{hline 20}{c +}{hline 35}
  Less than $25,000 {c |}{res}        140        9.40        9.40
{txt}  $25,000 - $49,999 {c |}{res}        260       17.45       26.85
{txt}  $50,000 - $74,999 {c |}{res}        297       19.93       46.78
{txt}  $75,000 - $99,999 {c |}{res}        245       16.44       63.22
{txt}$100,000 - $124,999 {c |}{res}        191       12.82       76.04
{txt}$125,000 - $149,999 {c |}{res}        136        9.13       85.17
{txt}$150,000 - $174,999 {c |}{res}         82        5.50       90.67
{txt}$175,000 - $199,999 {c |}{res}         43        2.89       93.56
{txt} More than $200,000 {c |}{res}         96        6.44      100.00
{txt}{hline 20}{c +}{hline 35}
              Total {c |}{res}      1,490      100.00
{txt}
{com}. 
. *Education
. tab education

                    {txt}Education {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
  Bachelor’s degree or higher {c |}{res}        890       58.75       58.75
{txt}                  High school {c |}{res}        172       11.35       70.10
{txt}        Less than high school {c |}{res}          9        0.59       70.69
{txt}         Prefer not to answer {c |}{res}          4        0.26       70.96
{txt}                 Some college {c |}{res}        440       29.04      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}      1,515      100.00
{txt}
{com}. gen Education=.
{txt}(1,515 missing values generated)

{com}. replace Education=1 if education=="Bachelor’s degree or higher"
{txt}(890 real changes made)

{com}. replace Education=0 if education!="Bachelor’s degree or higher"
{txt}(625 real changes made)

{com}. replace Education=. if education=="Prefer not to answer"
{txt}(4 real changes made, 4 to missing)

{com}. 
. label define Education 1 "Bachelor’s degree or higher" 0 "Less than Bachelor’s degree"
{txt}
{com}. label values Education Education
{txt}
{com}. tab Education

                  {txt}Education {c |}      Freq.     Percent        Cum.
{hline 28}{c +}{hline 35}
Less than Bachelor’s degree {c |}{res}        621       41.10       41.10
{txt}Bachelor’s degree or higher {c |}{res}        890       58.90      100.00
{txt}{hline 28}{c +}{hline 35}
                      Total {c |}{res}      1,511      100.00
{txt}
{com}. 
. *Partisanship
. tab partisanship

        {txt}Partisanship {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
            Democrat {c |}{res}        525       34.65       34.65
{txt}          Don't know {c |}{res}          2        0.13       34.79
{txt}         Independent {c |}{res}        498       32.87       67.66
{txt}         Other Party {c |}{res}          8        0.53       68.18
{txt}Prefer not to answer {c |}{res}          4        0.26       68.45
{txt}          Republican {c |}{res}        478       31.55      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}      1,515      100.00
{txt}
{com}. g Democrat=.
{txt}(1,515 missing values generated)

{com}. replace Democrat=1 if partisanship=="Democrat"
{txt}(525 real changes made)

{com}. replace Democrat=0 if partisanship!="Democrat"
{txt}(990 real changes made)

{com}. replace Democrat=. if partisanship=="Prefer not to answer"
{txt}(4 real changes made, 4 to missing)

{com}. label define Democrat 1 "Democrat" 0 "Not Democrat"
{txt}
{com}. label values Democrat Democrat
{txt}
{com}. tab Democrat

    {txt}Democrat {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Not Democrat {c |}{res}        986       65.25       65.25
{txt}    Democrat {c |}{res}        525       34.75      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,511      100.00
{txt}
{com}. 
. g Republican=.
{txt}(1,515 missing values generated)

{com}. replace Republican=1 if partisanship=="Republican"
{txt}(478 real changes made)

{com}. replace Republican=0 if partisanship!="Republican"
{txt}(1,037 real changes made)

{com}. replace Republican=. if partisanship=="Prefer not to answer"
{txt}(4 real changes made, 4 to missing)

{com}. label define Republican 1 "Republican" 0 "Not Republican"
{txt}
{com}. label values Republican Republican
{txt}
{com}. tab Republican

    {txt}Republican {c |}      Freq.     Percent        Cum.
{hline 15}{c +}{hline 35}
Not Republican {c |}{res}      1,033       68.37       68.37
{txt}    Republican {c |}{res}        478       31.63      100.00
{txt}{hline 15}{c +}{hline 35}
         Total {c |}{res}      1,511      100.00
{txt}
{com}. 
. *Voting
. tab voting

              {txt}Voting {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
                  No {c |}{res}        215       14.19       14.19
{txt}Prefer not to answer {c |}{res}         11        0.73       14.92
{txt}                 Yes {c |}{res}      1,289       85.08      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}      1,515      100.00
{txt}
{com}. gen Voting_Pres20=.
{txt}(1,515 missing values generated)

{com}. replace Voting_Pres20=1 if voting=="Yes"
{txt}(1,289 real changes made)

{com}. replace Voting_Pres20=0 if voting=="No"
{txt}(215 real changes made)

{com}. tab Voting_Pres20

{txt}Voting_Pres {c |}
         20 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        215       14.30       14.30
{txt}          1 {c |}{res}      1,289       85.70      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,504      100.00
{txt}
{com}. 
. *Feeling
. tab feeling_china 

                    {txt}Feeling_China {c |}      Freq.     Percent        Cum.
{hline 34}{c +}{hline 35}
Neither unfavorably nor favorably {c |}{res}        417       27.58       27.58
{txt}               Slightly favorably {c |}{res}        126        8.33       35.91
{txt}             Slightly unfavorably {c |}{res}        368       24.34       60.25
{txt}               Somewhat favorably {c |}{res}        103        6.81       67.06
{txt}             Somewhat unfavorably {c |}{res}        302       19.97       87.04
{txt}               Strongly favorably {c |}{res}         42        2.78       89.81
{txt}                 Very unfavorably {c |}{res}        154       10.19      100.00
{txt}{hline 34}{c +}{hline 35}
                            Total {c |}{res}      1,512      100.00
{txt}
{com}. gen Feeling_China=.
{txt}(1,515 missing values generated)

{com}. replace Feeling_China=1 if feeling_china=="Very unfavorably"
{txt}(154 real changes made)

{com}. replace Feeling_China=2 if feeling_china=="Somewhat unfavorably"
{txt}(302 real changes made)

{com}. replace Feeling_China=3 if feeling_china=="Slightly unfavorably"
{txt}(368 real changes made)

{com}. replace Feeling_China=4 if feeling_china=="Neither unfavorably nor favorably"
{txt}(417 real changes made)

{com}. replace Feeling_China=5 if feeling_china=="Slightly favorably"
{txt}(126 real changes made)

{com}. replace Feeling_China=6 if feeling_china=="Somewhat favorably"
{txt}(103 real changes made)

{com}. replace Feeling_China=7 if feeling_china=="Strongly favorably"
{txt}(42 real changes made)

{com}. tab Feeling_China

{txt}Feeling_Chi {c |}
         na {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        154       10.19       10.19
{txt}          2 {c |}{res}        302       19.97       30.16
{txt}          3 {c |}{res}        368       24.34       54.50
{txt}          4 {c |}{res}        417       27.58       82.08
{txt}          5 {c |}{res}        126        8.33       90.41
{txt}          6 {c |}{res}        103        6.81       97.22
{txt}          7 {c |}{res}         42        2.78      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,512      100.00
{txt}
{com}. 
. tab feeling_japan

                    {txt}Feeling_Japan {c |}      Freq.     Percent        Cum.
{hline 34}{c +}{hline 35}
Neither unfavorably nor favorably {c |}{res}        280       18.49       18.49
{txt}               Slightly favorably {c |}{res}        308       20.34       38.84
{txt}             Slightly unfavorably {c |}{res}         50        3.30       42.14
{txt}               Somewhat favorably {c |}{res}        492       32.50       74.64
{txt}             Somewhat unfavorably {c |}{res}         20        1.32       75.96
{txt}               Strongly favorably {c |}{res}        353       23.32       99.27
{txt}                 Very unfavorably {c |}{res}         11        0.73      100.00
{txt}{hline 34}{c +}{hline 35}
                            Total {c |}{res}      1,514      100.00
{txt}
{com}. gen Feeling_Japan=.
{txt}(1,515 missing values generated)

{com}. replace Feeling_Japan=1 if feeling_japan=="Very unfavorably"
{txt}(11 real changes made)

{com}. replace Feeling_Japan=2 if feeling_japan=="Somewhat unfavorably"
{txt}(20 real changes made)

{com}. replace Feeling_Japan=3 if feeling_japan=="Slightly unfavorably"
{txt}(50 real changes made)

{com}. replace Feeling_Japan=4 if feeling_japan=="Neither unfavorably nor favorably"
{txt}(280 real changes made)

{com}. replace Feeling_Japan=5 if feeling_japan=="Slightly favorably"
{txt}(308 real changes made)

{com}. replace Feeling_Japan=6 if feeling_japan=="Somewhat favorably"
{txt}(492 real changes made)

{com}. replace Feeling_Japan=7 if feeling_japan=="Strongly favorably"
{txt}(353 real changes made)

{com}. tab Feeling_Japan

{txt}Feeling_Jap {c |}
         an {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         11        0.73        0.73
{txt}          2 {c |}{res}         20        1.32        2.05
{txt}          3 {c |}{res}         50        3.30        5.35
{txt}          4 {c |}{res}        280       18.49       23.84
{txt}          5 {c |}{res}        308       20.34       44.19
{txt}          6 {c |}{res}        492       32.50       76.68
{txt}          7 {c |}{res}        353       23.32      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,514      100.00
{txt}
{com}. 
. *Knowledge
. tab knowledge_china

{txt}Knowledge_Chi {c |}
           na {c |}      Freq.     Percent        Cum.
{hline 14}{c +}{hline 35}
Deng Xiaoping {c |}{res}         14        1.17        1.17
{txt}    Hu Jintao {c |}{res}         15        1.26        2.43
{txt}  Jiang Zemin {c |}{res}         12        1.00        3.43
{txt}   XI Jinping {c |}{res}      1,154       96.57      100.00
{txt}{hline 14}{c +}{hline 35}
        Total {c |}{res}      1,195      100.00
{txt}
{com}. gen Knowledge_China=0
{txt}
{com}. replace Knowledge_China=1 if knowledge_china=="XI Jinping"
{txt}(1,154 real changes made)

{com}. 
. tab knowledge_japan

{txt}Knowledge_Japa {c |}
             n {c |}      Freq.     Percent        Cum.
{hline 15}{c +}{hline 35}
 Fumio Kishida {c |}{res}        491       65.12       65.12
{txt}    Shinzo Abe {c |}{res}        185       24.54       89.66
{txt}Yoshihide Suga {c |}{res}         31        4.11       93.77
{txt}Yoshihiko Noda {c |}{res}         47        6.23      100.00
{txt}{hline 15}{c +}{hline 35}
         Total {c |}{res}        754      100.00
{txt}
{com}. gen Knowledge_Japan=0
{txt}
{com}. replace Knowledge_Japan=1 if knowledge_japan=="Fumio Kishida"
{txt}(491 real changes made)

{com}. 
. *Commitment credibility (Contunious)
. gen credibility=.
{txt}(1,515 missing values generated)

{com}. replace credibility=1 if credibility_g1=="Very unlikely"|credibility_g2=="Very unlikely"|credibility_g3=="Very unlikely"|credibility_g4=="Very unlikely"
{txt}(275 real changes made)

{com}. replace credibility=2 if credibility_g1=="Somewhat unlikely"|credibility_g2=="Somewhat unlikely"|credibility_g3=="Somewhat unlikely"|credibility_g4=="Somewhat unlikely"
{txt}(448 real changes made)

{com}. replace credibility=3 if credibility_g1=="Somewhat likely"|credibility_g2=="Somewhat likely"|credibility_g3=="Somewhat likely"|credibility_g4=="Somewhat likely"
{txt}(610 real changes made)

{com}. replace credibility=4 if credibility_g1=="Very likely"|credibility_g1=="Very likely"|credibility_g3=="Very likely"|credibility_g4=="Very likely"
{txt}(174 real changes made)

{com}. 
. *Credibility (Dummy)
. gen credibility_dummy=.
{txt}(1,515 missing values generated)

{com}. replace credibility_dummy=0 if credibility_g1=="Very unlikely"|credibility_g2=="Very unlikely"|credibility_g3=="Very unlikely"|credibility_g4=="Very unlikely"
{txt}(275 real changes made)

{com}. replace credibility_dummy=0 if credibility_g1=="Somewhat unlikely"|credibility_g2=="Somewhat unlikely"|credibility_g3=="Somewhat unlikely"|credibility_g4=="Somewhat unlikely"
{txt}(448 real changes made)

{com}. replace credibility_dummy=1 if credibility_g1=="Somewhat likely"|credibility_g2=="Somewhat likely"|credibility_g3=="Somewhat likely"|credibility_g4=="Somewhat likely"
{txt}(610 real changes made)

{com}. replace credibility_dummy=1 if credibility_g1=="Very likely"|credibility_g1=="Very likely"|credibility_g3=="Very likely"|credibility_g4=="Very likely"
{txt}(174 real changes made)

{com}. 
. *Support Change?
. gen support=.
{txt}(1,515 missing values generated)

{com}. replace support=1 if support_g1=="Strongly oppose"|support_g2=="Strongly oppose"|support_g3=="Strongly oppose"|support_g4=="Strongly oppose"
{txt}(375 real changes made)

{com}. replace support=2 if support_g1=="Somewhat oppose"|support_g2=="Somewhat oppose"|support_g3=="Somewhat oppose"|support_g4=="Somewhat oppose"
{txt}(375 real changes made)

{com}. replace support=3 if support_g1=="Don't care/ Neither support nor oppose"|support_g2=="Don't care/ Neither support nor oppose"|support_g3=="Don't care/ Neither support nor oppose"|support_g4=="Don't care/ Neither support nor oppose"
{txt}(243 real changes made)

{com}. replace support=4 if support_g1=="Somewhat support"|support_g2=="Somewhat support"|support_g3=="Somewhat support"|support_g4=="Somewhat support"
{txt}(396 real changes made)

{com}. replace support=5 if support_g1=="Strongly support"|support_g2=="Strongly support"|support_g3=="Strongly support"|support_g4=="Strongly support"
{txt}(126 real changes made)

{com}. 
. **Work with China/Japan?
. gen collaborate=.
{txt}(1,515 missing values generated)

{com}. replace collaborate=1 if work_g1=="Strongly disagree"|work_g2=="Strongly disagree"|work_g3=="Strongly disagree"|work_g4=="Strongly disagree"
{txt}(86 real changes made)

{com}. replace collaborate=2 if work_g1=="Somewhat disagree"|work_g2=="Somewhat disagree"|work_g3=="Somewhat disagree"|work_g4=="Somewhat disagree"
{txt}(190 real changes made)

{com}. replace collaborate=3 if work_g1=="Don't care/ Neither agree nor disagree"|work_g2=="Don't care/ Neither agree nor disagree"|work_g3=="Don't care/ Neither agree nor disagree"|work_g4=="Don't care/ Neither agree nor disagree"
{txt}(252 real changes made)

{com}. replace collaborate=4 if work_g1=="Somewhat agree"|work_g2=="Somewhat agree"|work_g3=="Somewhat agree"|work_g4=="Somewhat agree"
{txt}(590 real changes made)

{com}. replace collaborate=5 if work_g1=="Strongly agree"|work_g2=="Strongly agree"|work_g3=="Strongly agree"|work_g4=="Strongly agree"
{txt}(397 real changes made)

{com}. 
. *Trust Government?
. gen trust_gov=.
{txt}(1,515 missing values generated)

{com}. replace trust_gov=1 if trust_gov_g1=="Strongly distrust"|trust_gov_g2=="Strongly distrust"|trust_gov_g3=="Strongly distrust"|trust_gov_g4=="Strongly distrust"
{txt}(311 real changes made)

{com}. replace trust_gov=2 if trust_gov_g1=="Somewhat distrust"|trust_gov_g2=="Somewhat distrust"|trust_gov_g3=="Somewhat distrust"|trust_gov_g4=="Somewhat distrust"
{txt}(427 real changes made)

{com}. replace trust_gov=3 if trust_gov_g1=="Don't care/ Neither trust nor distrust"|trust_gov_g2=="Don't care/ Neither trust nor distrust"|trust_gov_g3=="Don't care/ Neither trust nor distrust"|trust_gov_g4=="Don't care/ Neither trust nor distrust"
{txt}(300 real changes made)

{com}. replace trust_gov=4 if trust_gov_g1=="Somewhat trust"|trust_gov_g2=="Somewhat trust"|trust_gov_g3=="Somewhat trust"|trust_gov_g4=="Somewhat trust"
{txt}(375 real changes made)

{com}. replace trust_gov=5 if trust_gov_g1=="Strongly trust"|trust_gov_g2=="Strongly trust"|trust_gov_g3=="Strongly trust"|trust_gov_g4=="Strongly trust"
{txt}(102 real changes made)

{com}. 
. *Trust Citizens?
. gen trust_cit=.
{txt}(1,515 missing values generated)

{com}. replace trust_cit=1 if trust_cit_g1=="Strongly distrust"|trust_cit_g2=="Strongly distrust"|trust_cit_g3=="Strongly distrust"|trust_cit_g4=="Strongly distrust"
{txt}(59 real changes made)

{com}. replace trust_cit=2 if trust_cit_g1=="Somewhat distrust"|trust_cit_g2=="Somewhat distrust"|trust_cit_g3=="Somewhat distrust"|trust_cit_g4=="Somewhat distrust"
{txt}(185 real changes made)

{com}. replace trust_cit=3 if trust_cit_g1=="Don't care/ Neither trust nor distrust"|trust_cit_g2=="Don't care/ Neither trust nor distrust"|trust_cit_g3=="Don't care/ Neither trust nor distrust"|trust_cit_g4=="Don't care/ Neither trust nor distrust"
{txt}(517 real changes made)

{com}. replace trust_cit=4 if trust_cit_g1=="Somewhat trust"|trust_cit_g2=="Somewhat trust"|trust_cit_g3=="Somewhat trust"|trust_cit_g4=="Somewhat trust"
{txt}(494 real changes made)

{com}. replace trust_cit=5 if trust_cit_g1=="Strongly trust"|trust_cit_g2=="Strongly trust"|trust_cit_g3=="Strongly trust"|trust_cit_g4=="Strongly trust"
{txt}(260 real changes made)

{com}. 
. **Table 1
. tab condition

          {txt}condition {c |}      Freq.     Percent        Cum.
{hline 20}{c +}{hline 35}
China/No Commitment {c |}{res}        379       25.02       25.02
{txt}   China/Commitment {c |}{res}        375       24.75       49.77
{txt}Japan/No Commitmnet {c |}{res}        382       25.21       74.98
{txt}   Japan/Commitment {c |}{res}        379       25.02      100.00
{txt}{hline 20}{c +}{hline 35}
              Total {c |}{res}      1,515      100.00
{txt}
{com}. 
. **Table 2
. ttest credibility if condition==1 | condition==2, by(condition)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
China/No {c |}{res}{col 12}    379{col 22} 2.102902{col 34} .0441292{col 46} .8591044{col 58} 2.016133{col 70} 2.189672
{txt}China/Co {c |}{res}{col 12}    367{col 22} 1.705722{col 34} .0358068{col 46} .6859599{col 58} 1.635309{col 70} 1.776135
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    746{col 22} 1.907507{col 34} .0294071{col 46} .8031966{col 58} 1.849776{col 70} 1.965237
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .3971803{col 34} .0570318{col 58} .2852179{col 70} .5091427
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}China/No{txt}) - mean({res}China/Co{txt})                        t = {res}  6.9642
{txt}Ho: diff = 0                                     degrees of freedom = {res}     744

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest credibility_dummy if condition==1 | condition==2, by(condition)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
China/No {c |}{res}{col 12}    379{col 22} .3087071{col 34} .0237607{col 46} .4625706{col 58} .2619875{col 70} .3554267
{txt}China/Co {c |}{res}{col 12}    367{col 22} .1307902{col 34} .0176242{col 46} .3376311{col 58} .0961328{col 70} .1654476
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    746{col 22} .2211796{col 34} .0152059{col 46} .4153197{col 58}  .191328{col 70} .2510312
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .1779169{col 34} .0297291{col 58} .1195541{col 70} .2362798
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}China/No{txt}) - mean({res}China/Co{txt})                        t = {res}  5.9846
{txt}Ho: diff = 0                                     degrees of freedom = {res}     744

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. **Table 3
. ttest credibility if condition==3 | condition==4, by(condition)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Japan/No {c |}{res}{col 12}    382{col 22} 3.070681{col 34} .0358536{col 46} .7007525{col 58} 3.000185{col 70} 3.141176
{txt}Japan/Co {c |}{res}{col 12}    379{col 22} 2.905013{col 34} .0332705{col 46} .6477075{col 58} 2.839595{col 70} 2.970432
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    761{col 22} 2.988173{col 34} .0246314{col 46} .6794866{col 58}  2.93982{col 70} 3.036527
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .1656674{col 34} .0489274{col 58} .0696183{col 70} .2617166
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Japan/No{txt}) - mean({res}Japan/Co{txt})                        t = {res}  3.3860
{txt}Ho: diff = 0                                     degrees of freedom = {res}     759

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9996         {txt}Pr(|T| > |t|) = {res}0.0007          {txt}Pr(T > t) = {res}0.0004
{txt}
{com}. ttest credibility_dummy if condition==3 | condition==4, by(condition)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Japan/No {c |}{res}{col 12}    382{col 22} .8403141{col 34} .0187669{col 46} .3667949{col 58} .8034146{col 70} .8772137
{txt}Japan/Co {c |}{res}{col 12}    379{col 22} .7862797{col 34} .0210846{col 46} .4104735{col 58} .7448219{col 70} .8277375
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    761{col 22} .8134034{col 34} .0141318{col 46} .3898436{col 58} .7856614{col 70} .8411455
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0540345{col 34} .0282144{col 58} -.001353{col 70} .1094219
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Japan/No{txt}) - mean({res}Japan/Co{txt})                        t = {res}  1.9151
{txt}Ho: diff = 0                                     degrees of freedom = {res}     759

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9721         {txt}Pr(|T| > |t|) = {res}0.0559          {txt}Pr(T > t) = {res}0.0279
{txt}
{com}. 
. **Multiple Comparison
. anova credibility condition

                         {txt}Number of obs = {res}     1,507    {txt}R-squared     ={res}  0.3733
                         {txt}Root MSE      =   {res} .728142    {txt}Adj R-squared ={res}  0.3720

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res}   474.575          3   158.19167    298.37  0.0000
                         {txt}{c |}
               condition {c |} {res}   474.575          3   158.19167    298.37  0.0000
                         {txt}{c |}
                Residual {c |} {res} 796.87689      1,503   .53019088  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 1271.4519      1,506   .84425756  
{txt}
{com}. pwcompare condition, effects sort mcompare(tukey)
{res}
{txt}Pairwise comparisons of marginal linear predictions

{txt}{p2colset 1 14 16 2}{...}
{p2col:Margins}:{space 1}{res:asbalanced}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 13}
{col 14}{c |}    Number of
{col 14}{c |}  Comparisons
{hline 13}{c +}{hline 13}
{space 3}condition {c |}{col 14}{res}{space 1}           6
{txt}{hline 13}{c BT}{hline 13}

{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 68}        T{col 77}ukey{col 85}            T{col 98}ukey
{col 45}{c |}   Contrast{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 34}condition {c |}
{space 3}China/Commitment vs China/No Commitment  {c |}{col 45}{res}{space 2}-.3971803{col 57}{space 2} .0533253{col 68}{space 1}   -7.45{col 77}{space 3}0.000{col 85}{space 4}-.5343279{col 98}{space 3}-.2600327
{txt}{space 3}Japan/Commitment vs Japan/No Commitmnet  {c |}{col 45}{res}{space 2}-.1656674{col 57}{space 2} .0527907{col 68}{space 1}   -3.14{col 77}{space 3}0.009{col 85}{space 4}-.3014402{col 98}{space 3}-.0298947
{txt}{space 3}Japan/Commitment vs China/No Commitment  {c |}{col 45}{res}{space 2} .8021108{col 57}{space 2} .0528946{col 68}{space 1}   15.16{col 77}{space 3}0.000{col 85}{space 4} .6660707{col 98}{space 3} .9381509
{txt}Japan/No Commitmnet vs China/No Commitment  {c |}{col 45}{res}{space 2} .9677783{col 57}{space 2} .0527907{col 68}{space 1}   18.33{col 77}{space 3}0.000{col 85}{space 4} .8320055{col 98}{space 3} 1.103551
{txt}{space 6}Japan/Commitment vs China/Commitment  {c |}{col 45}{res}{space 2} 1.199291{col 57}{space 2} .0533253{col 68}{space 1}   22.49{col 77}{space 3}0.000{col 85}{space 4} 1.062143{col 98}{space 3} 1.336439
{txt}{space 3}Japan/No Commitmnet vs China/Commitment  {c |}{col 45}{res}{space 2} 1.364959{col 57}{space 2} .0532221{col 68}{space 1}   25.65{col 77}{space 3}0.000{col 85}{space 4} 1.228076{col 98}{space 3} 1.501841
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. anova credibility_dummy condition

                         {txt}Number of obs = {res}     1,507    {txt}R-squared     ={res}  0.3684
                         {txt}Root MSE      =   {res} .397557    {txt}Adj R-squared ={res}  0.3672

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 138.58156          3   46.193853    292.27  0.0000
                         {txt}{c |}
               condition {c |} {res} 138.58156          3   46.193853    292.27  0.0000
                         {txt}{c |}
                Residual {c |} {res} 237.55115      1,503   .15805133  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 376.13271      1,506   .24975612  
{txt}
{com}. pwcompare condition, effects sort mcompare(tukey)
{res}
{txt}Pairwise comparisons of marginal linear predictions

{txt}{p2colset 1 14 16 2}{...}
{p2col:Margins}:{space 1}{res:asbalanced}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 13}
{col 14}{c |}    Number of
{col 14}{c |}  Comparisons
{hline 13}{c +}{hline 13}
{space 3}condition {c |}{col 14}{res}{space 1}           6
{txt}{hline 13}{c BT}{hline 13}

{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 68}        T{col 77}ukey{col 85}            T{col 98}ukey
{col 45}{c |}   Contrast{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 34}condition {c |}
{space 3}China/Commitment vs China/No Commitment  {c |}{col 45}{res}{space 2}-.1779169{col 57}{space 2} .0291149{col 68}{space 1}   -6.11{col 77}{space 3}0.000{col 85}{space 4}-.2527979{col 98}{space 3} -.103036
{txt}{space 3}Japan/Commitment vs Japan/No Commitmnet  {c |}{col 45}{res}{space 2}-.0540345{col 57}{space 2} .0288231{col 68}{space 1}   -1.87{col 77}{space 3}0.239{col 85}{space 4}-.1281647{col 98}{space 3} .0200958
{txt}{space 3}Japan/Commitment vs China/No Commitment  {c |}{col 45}{res}{space 2} .4775726{col 57}{space 2} .0288798{col 68}{space 1}   16.54{col 77}{space 3}0.000{col 85}{space 4} .4032963{col 98}{space 3} .5518488
{txt}Japan/No Commitmnet vs China/No Commitment  {c |}{col 45}{res}{space 2}  .531607{col 57}{space 2} .0288231{col 68}{space 1}   18.44{col 77}{space 3}0.000{col 85}{space 4} .4574768{col 98}{space 3} .6057373
{txt}{space 6}Japan/Commitment vs China/Commitment  {c |}{col 45}{res}{space 2} .6554895{col 57}{space 2} .0291149{col 68}{space 1}   22.51{col 77}{space 3}0.000{col 85}{space 4} .5806086{col 98}{space 3} .7303704
{txt}{space 3}Japan/No Commitmnet vs China/Commitment  {c |}{col 45}{res}{space 2} .7095239{col 57}{space 2} .0290586{col 68}{space 1}   24.42{col 77}{space 3}0.000{col 85}{space 4} .6347878{col 98}{space 3} .7842601
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. **Tests for the Conditional Effect (Main Text and Table A4 in Appendix)
. gen China_Commitment=0
{txt}
{com}. replace China_Commitment=1 if condition==2
{txt}(375 real changes made)

{com}. label define China_Commitmemt 0 "Extremely Conservative" 2 "Conservative" 3 "Slightly Conservative" 4 "Moderate, Middle of the Road/Don't Know" 5 "Slightly" 
{txt}
{com}. 
. gen Japan_NoCommitment=0
{txt}
{com}. replace Japan_NoCommitment=1 if condition==3
{txt}(382 real changes made)

{com}. 
. gen Japan_Commitment=0
{txt}
{com}. replace Japan_Commitment=1 if condition==4
{txt}(379 real changes made)

{com}. 
. reg credibility China_Commitment Japan_NoCommitment Japan_Commitment

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,507
{txt}{hline 13}{c +}{hline 34}   F(3, 1503)      = {res}   298.37
{txt}       Model {c |} {res} 474.575002         3  158.191667   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 796.876889     1,503  .530190878   {txt}R-squared       ={res}    0.3733
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.3720
{txt}       Total {c |} {res} 1271.45189     1,506  .844257564   {txt}Root MSE        =   {res} .72814

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       credibility{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}China_Commitment {c |}{col 20}{res}{space 2}-.3971803{col 32}{space 2} .0533253{col 43}{space 1}   -7.45{col 52}{space 3}0.000{col 60}{space 4}-.5017801{col 73}{space 3}-.2925805
{txt}Japan_NoCommitment {c |}{col 20}{res}{space 2} .9677783{col 32}{space 2} .0527907{col 43}{space 1}   18.33{col 52}{space 3}0.000{col 60}{space 4} .8642271{col 73}{space 3} 1.071329
{txt}{space 2}Japan_Commitment {c |}{col 20}{res}{space 2} .8021108{col 32}{space 2} .0528946{col 43}{space 1}   15.16{col 52}{space 3}0.000{col 60}{space 4} .6983557{col 73}{space 3} .9058659
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} 2.102902{col 32}{space 2} .0374021{col 43}{space 1}   56.22{col 52}{space 3}0.000{col 60}{space 4} 2.029536{col 73}{space 3} 2.176268
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model1
{txt}
{com}. lincom _b[China_Commitment]-(_b[Japan_Commitment]-_b[Japan_NoCommitment])

{p 0 7}{space 1}{text:( 1)}{space 1} {res}China_Commitment + Japan_NoCommitment - Japan_Commitment = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} credibility{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.2315129{col 26}{space 2} .0750362{col 37}{space 1}   -3.09{col 46}{space 3}0.002{col 54}{space 4}-.3786997{col 67}{space 3} -.084326
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. reg credibility_dummy China_Commitment Japan_NoCommitment Japan_Commitment

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,507
{txt}{hline 13}{c +}{hline 34}   F(3, 1503)      = {res}   292.27
{txt}       Model {c |} {res}  138.58156         3  46.1938533   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 237.551154     1,503  .158051333   {txt}R-squared       ={res}    0.3684
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.3672
{txt}       Total {c |} {res} 376.132714     1,506  .249756118   {txt}Root MSE        =   {res} .39756

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} credibility_dummy{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}China_Commitment {c |}{col 20}{res}{space 2}-.1779169{col 32}{space 2} .0291149{col 43}{space 1}   -6.11{col 52}{space 3}0.000{col 60}{space 4}-.2350272{col 73}{space 3}-.1208067
{txt}Japan_NoCommitment {c |}{col 20}{res}{space 2}  .531607{col 32}{space 2} .0288231{col 43}{space 1}   18.44{col 52}{space 3}0.000{col 60}{space 4} .4750693{col 73}{space 3} .5881447
{txt}{space 2}Japan_Commitment {c |}{col 20}{res}{space 2} .4775726{col 32}{space 2} .0288798{col 43}{space 1}   16.54{col 52}{space 3}0.000{col 60}{space 4} .4209235{col 73}{space 3} .5342216
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} .3087071{col 32}{space 2} .0204211{col 43}{space 1}   15.12{col 52}{space 3}0.000{col 60}{space 4} .2686502{col 73}{space 3}  .348764
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model2
{txt}
{com}. lincom _b[China_Commitment]+(_b[Japan_NoCommitment]-_b[Japan_Commitment])

{p 0 7}{space 1}{text:( 1)}{space 1} {res}China_Commitment + Japan_NoCommitment - Japan_Commitment = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}credibili~my{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.1238825{col 26}{space 2} .0409689{col 37}{space 1}   -3.02{col 46}{space 3}0.003{col 54}{space 4}-.2042447{col 67}{space 3}-.0435203
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Alternavite: Interaction Terms (Table A4 in Appendix)
. gen China=0
{txt}
{com}. replace China=1 if condition==1|condition==2
{txt}(754 real changes made)

{com}. 
. gen Commitment=0
{txt}
{com}. replace Commitment=1 if condition==2|condition==4
{txt}(754 real changes made)

{com}. 
. reg credibility i.China##i.Commitment

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,507
{txt}{hline 13}{c +}{hline 34}   F(3, 1503)      = {res}   298.37
{txt}       Model {c |} {res} 474.575002         3  158.191667   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 796.876889     1,503  .530190878   {txt}R-squared       ={res}    0.3733
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.3720
{txt}       Total {c |} {res} 1271.45189     1,506  .844257564   {txt}Root MSE        =   {res} .72814

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     credibility{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}1.China {c |}{col 18}{res}{space 2}-.9677783{col 30}{space 2} .0527907{col 41}{space 1}  -18.33{col 50}{space 3}0.000{col 58}{space 4}-1.071329{col 71}{space 3}-.8642271
{txt}{space 4}1.Commitment {c |}{col 18}{res}{space 2}-.1656674{col 30}{space 2} .0527907{col 41}{space 1}   -3.14{col 50}{space 3}0.002{col 58}{space 4}-.2692186{col 71}{space 3}-.0621162
{txt}{space 16} {c |}
China#Commitment {c |}
{space 12}1 1  {c |}{col 18}{res}{space 2}-.2315129{col 30}{space 2} .0750362{col 41}{space 1}   -3.09{col 50}{space 3}0.002{col 58}{space 4}-.3786997{col 71}{space 3} -.084326
{txt}{space 16} {c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 3.070681{col 30}{space 2}  .037255{col 41}{space 1}   82.42{col 50}{space 3}0.000{col 58}{space 4} 2.997603{col 71}{space 3} 3.143758
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model3
{txt}
{com}. 
. reg credibility_dummy i.China##i.Commitment

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,507
{txt}{hline 13}{c +}{hline 34}   F(3, 1503)      = {res}   292.27
{txt}       Model {c |} {res}  138.58156         3  46.1938533   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 237.551154     1,503  .158051333   {txt}R-squared       ={res}    0.3684
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.3672
{txt}       Total {c |} {res} 376.132714     1,506  .249756118   {txt}Root MSE        =   {res} .39756

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}credibility_du~y{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}1.China {c |}{col 18}{res}{space 2} -.531607{col 30}{space 2} .0288231{col 41}{space 1}  -18.44{col 50}{space 3}0.000{col 58}{space 4}-.5881447{col 71}{space 3}-.4750693
{txt}{space 4}1.Commitment {c |}{col 18}{res}{space 2}-.0540345{col 30}{space 2} .0288231{col 41}{space 1}   -1.87{col 50}{space 3}0.061{col 58}{space 4}-.1105721{col 71}{space 3} .0025032
{txt}{space 16} {c |}
China#Commitment {c |}
{space 12}1 1  {c |}{col 18}{res}{space 2}-.1238825{col 30}{space 2} .0409689{col 41}{space 1}   -3.02{col 50}{space 3}0.003{col 58}{space 4}-.2042447{col 71}{space 3}-.0435203
{txt}{space 16} {c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .8403141{col 30}{space 2} .0203408{col 41}{space 1}   41.31{col 50}{space 3}0.000{col 58}{space 4} .8004148{col 71}{space 3} .8802134
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model4
{txt}
{com}. 
. esttab Model1 Model2 Model3 Model4 using conditional_effect.tex, replace se r2 legend label collabels(none) varlabels(_cons Constant) star(* 0.05 ** 0.01)
{res}{txt}(output written to {browse  `"conditional_effect.tex"'})

{com}. 
. **Table 4 and 5: Alternative Measures of International Reputation Costs
. ttest support if condition==1 | condition==2, by(condition)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
China/No {c |}{res}{col 12}    379{col 22} 1.849604{col 34} .0494577{col 46} .9628378{col 58} 1.752358{col 70} 1.946851
{txt}China/Co {c |}{res}{col 12}    375{col 22} 1.754667{col 34} .0487585{col 46} .9442046{col 58} 1.658791{col 70} 1.850542
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    754{col 22} 1.802387{col 34} .0347487{col 46} .9541659{col 58} 1.734171{col 70} 1.870603
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0949376{col 34} .0694583{col 58}-.0414177{col 70} .2312928
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}China/No{txt}) - mean({res}China/Co{txt})                        t = {res}  1.3668
{txt}Ho: diff = 0                                     degrees of freedom = {res}     752

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9140         {txt}Pr(|T| > |t|) = {res}0.1721          {txt}Pr(T > t) = {res}0.0860
{txt}
{com}. ttest support if condition==3 | condition==4, by(condition)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Japan/No {c |}{res}{col 12}    382{col 22} 3.638743{col 34} .0507022{col 46} .9909652{col 58} 3.539052{col 70} 3.738435
{txt}Japan/Co {c |}{res}{col 12}    379{col 22} 3.480211{col 34} .0522157{col 46} 1.016532{col 58} 3.377541{col 70} 3.582881
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    761{col 22}  3.55979{col 34} .0364765{col 46}  1.00625{col 58} 3.488183{col 70} 3.631396
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .1585324{col 34} .0727745{col 58} .0156691{col 70} .3013956
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Japan/No{txt}) - mean({res}Japan/Co{txt})                        t = {res}  2.1784
{txt}Ho: diff = 0                                     degrees of freedom = {res}     759

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9852         {txt}Pr(|T| > |t|) = {res}0.0297          {txt}Pr(T > t) = {res}0.0148
{txt}
{com}. 
. ttest collaborate if condition==1 | condition==2, by(condition)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
China/No {c |}{res}{col 12}    379{col 22} 3.395778{col 34} .0603995{col 46} 1.175852{col 58} 3.277017{col 70} 3.514539
{txt}China/Co {c |}{res}{col 12}    375{col 22} 3.394667{col 34} .0621418{col 46}  1.20337{col 58} 3.272476{col 70} 3.516858
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    754{col 22} 3.395225{col 34} .0432945{col 46} 1.188827{col 58} 3.310233{col 70} 3.480218
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0011117{col 34} .0866478{col 58}-.1689887{col 70} .1712121
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}China/No{txt}) - mean({res}China/Co{txt})                        t = {res}  0.0128
{txt}Ho: diff = 0                                     degrees of freedom = {res}     752

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.5051         {txt}Pr(|T| > |t|) = {res}0.9898          {txt}Pr(T > t) = {res}0.4949
{txt}
{com}. ttest collaborate if condition==3 | condition==4, by(condition)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Japan/No {c |}{res}{col 12}    382{col 22} 3.984293{col 34} .0524185{col 46}  1.02451{col 58} 3.881227{col 70} 4.087359
{txt}Japan/Co {c |}{res}{col 12}    379{col 22} 3.918206{col 34} .0554485{col 46} 1.079467{col 58}  3.80918{col 70} 4.027232
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    761{col 22}  3.95138{col 34} .0381374{col 46} 1.052066{col 58} 3.876513{col 70} 4.026247
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0660874{col 34} .0762879{col 58}-.0836729{col 70} .2158477
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Japan/No{txt}) - mean({res}Japan/Co{txt})                        t = {res}  0.8663
{txt}Ho: diff = 0                                     degrees of freedom = {res}     759

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.8067         {txt}Pr(|T| > |t|) = {res}0.3866          {txt}Pr(T > t) = {res}0.1933
{txt}
{com}. 
. ttest trust_gov if condition==1 | condition==2, by(condition)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
China/No {c |}{res}{col 12}    379{col 22} 1.955145{col 34} .0487333{col 46} .9487355{col 58} 1.859323{col 70} 2.050967
{txt}China/Co {c |}{res}{col 12}    375{col 22}    1.792{col 34}   .04514{col 46} .8741321{col 58}  1.70324{col 70}  1.88076
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    754{col 22} 1.874005{col 34} .0333382{col 46} .9154354{col 58} 1.808558{col 70} 1.939452
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .1631451{col 34} .0664559{col 58}  .032684{col 70} .2936062
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}China/No{txt}) - mean({res}China/Co{txt})                        t = {res}  2.4549
{txt}Ho: diff = 0                                     degrees of freedom = {res}     752

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9928         {txt}Pr(|T| > |t|) = {res}0.0143          {txt}Pr(T > t) = {res}0.0072
{txt}
{com}. ttest trust_gov if condition==3 | condition==4, by(condition)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Japan/No {c |}{res}{col 12}    382{col 22} 3.568063{col 34} .0478855{col 46} .9359128{col 58}  3.47391{col 70} 3.662216
{txt}Japan/Co {c |}{res}{col 12}    379{col 22} 3.427441{col 34} .0487703{col 46} .9494563{col 58} 3.331546{col 70} 3.523336
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    761{col 22} 3.498029{col 34} .0342448{col 46} .9446853{col 58} 3.430803{col 70} 3.565255
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .1406222{col 34} .0683449{col 58} .0064546{col 70} .2747897
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Japan/No{txt}) - mean({res}Japan/Co{txt})                        t = {res}  2.0575
{txt}Ho: diff = 0                                     degrees of freedom = {res}     759

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9800         {txt}Pr(|T| > |t|) = {res}0.0400          {txt}Pr(T > t) = {res}0.0200
{txt}
{com}. 
. ttest trust_cit if condition==1 | condition==2, by(condition)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
China/No {c |}{res}{col 12}    379{col 22} 3.023747{col 34} .0480516{col 46} .9354656{col 58} 2.929265{col 70} 3.118229
{txt}China/Co {c |}{res}{col 12}    375{col 22} 2.906667{col 34} .0489267{col 46} .9474612{col 58} 2.810461{col 70} 3.002873
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    754{col 22} 2.965517{col 34} .0343292{col 46} .9426471{col 58} 2.898125{col 70}  3.03291
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}   .11708{col 34} .0685722{col 58}-.0175356{col 70} .2516957
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}China/No{txt}) - mean({res}China/Co{txt})                        t = {res}  1.7074
{txt}Ho: diff = 0                                     degrees of freedom = {res}     752

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9559         {txt}Pr(|T| > |t|) = {res}0.0882          {txt}Pr(T > t) = {res}0.0441
{txt}
{com}. ttest trust_cit if condition==3 | condition==4, by(condition)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Japan/No {c |}{res}{col 12}    382{col 22} 3.986911{col 34} .0451686{col 46} .8828113{col 58}   3.8981{col 70} 4.075722
{txt}Japan/Co {c |}{res}{col 12}    379{col 22} 3.949868{col 34} .0437368{col 46} .8514654{col 58}  3.86387{col 70} 4.035866
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    761{col 22} 3.968463{col 34} .0314276{col 46} .8669692{col 58} 3.906767{col 70} 4.030158
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0370429{col 34} .0628827{col 58}-.0864018{col 70} .1604877
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Japan/No{txt}) - mean({res}Japan/Co{txt})                        t = {res}  0.5891
{txt}Ho: diff = 0                                     degrees of freedom = {res}     759

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.7220         {txt}Pr(|T| > |t|) = {res}0.5560          {txt}Pr(T > t) = {res}0.2780
{txt}
{com}. 
. *Tests for the Conditional Effect (Table A5 in Appendix)
. reg support China_Commitment Japan_NoCommitment Japan_Commitment

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,515
{txt}{hline 13}{c +}{hline 34}   F(3, 1511)      = {res}   408.96
{txt}       Model {c |} {res} 1176.21089         3  392.070296   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 1448.60495     1,511  .958706124   {txt}R-squared       ={res}    0.4481
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.4470
{txt}       Total {c |} {res} 2624.81584     1,514  1.73369606   {txt}Root MSE        =   {res} .97914

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           support{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}China_Commitment {c |}{col 20}{res}{space 2}-.0949376{col 32}{space 2}  .071317{col 43}{space 1}   -1.33{col 52}{space 3}0.183{col 60}{space 4}-.2348284{col 73}{space 3} .0449533
{txt}Japan_NoCommitment {c |}{col 20}{res}{space 2} 1.789139{col 32}{space 2} .0709878{col 43}{space 1}   25.20{col 52}{space 3}0.000{col 60}{space 4} 1.649894{col 73}{space 3} 1.928384
{txt}{space 2}Japan_Commitment {c |}{col 20}{res}{space 2} 1.630607{col 32}{space 2} .0711276{col 43}{space 1}   22.93{col 52}{space 3}0.000{col 60}{space 4} 1.491088{col 73}{space 3} 1.770126
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} 1.849604{col 32}{space 2} .0502948{col 43}{space 1}   36.78{col 52}{space 3}0.000{col 60}{space 4} 1.750949{col 73}{space 3} 1.948259
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model1
{txt}
{com}. lincom _b[China_Commitment]-(_b[Japan_Commitment]-_b[Japan_NoCommitment])

{p 0 7}{space 1}{text:( 1)}{space 1} {res}China_Commitment + Japan_NoCommitment - Japan_Commitment = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     support{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0635948{col 26}{space 2}  .100625{col 37}{space 1}    0.63{col 46}{space 3}0.527{col 54}{space 4}-.1337846{col 67}{space 3} .2609743
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. reg collaborate China_Commitment Japan_NoCommitment Japan_Commitment

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,515
{txt}{hline 13}{c +}{hline 34}   F(3, 1511)      = {res}    31.20
{txt}       Model {c |} {res} 117.978899         3  39.3262998   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 1904.59272     1,511  1.26048492   {txt}R-squared       ={res}    0.0583
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0565
{txt}       Total {c |} {res} 2022.57162     1,514  1.33591256   {txt}Root MSE        =   {res} 1.1227

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       collaborate{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}China_Commitment {c |}{col 20}{res}{space 2}-.0011117{col 32}{space 2} .0817748{col 43}{space 1}   -0.01{col 52}{space 3}0.989{col 60}{space 4}-.1615158{col 73}{space 3} .1592924
{txt}Japan_NoCommitment {c |}{col 20}{res}{space 2} .5885148{col 32}{space 2} .0813973{col 43}{space 1}    7.23{col 52}{space 3}0.000{col 60}{space 4} .4288512{col 73}{space 3} .7481785
{txt}{space 2}Japan_Commitment {c |}{col 20}{res}{space 2} .5224274{col 32}{space 2} .0815576{col 43}{space 1}    6.41{col 52}{space 3}0.000{col 60}{space 4} .3624494{col 73}{space 3} .6824055
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} 3.395778{col 32}{space 2} .0576699{col 43}{space 1}   58.88{col 52}{space 3}0.000{col 60}{space 4} 3.282657{col 73}{space 3}   3.5089
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model2
{txt}
{com}. lincom _b[China_Commitment]+(_b[Japan_NoCommitment]-_b[Japan_Commitment])

{p 0 7}{space 1}{text:( 1)}{space 1} {res}China_Commitment + Japan_NoCommitment - Japan_Commitment = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} collaborate{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0649757{col 26}{space 2} .1153804{col 37}{space 1}    0.56{col 46}{space 3}0.573{col 54}{space 4}-.1613469{col 67}{space 3} .2912983
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. reg trust_gov China_Commitment Japan_NoCommitment Japan_Commitment

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,515
{txt}{hline 13}{c +}{hline 34}   F(3, 1511)      = {res}   390.27
{txt}       Model {c |} {res} 1007.69297         3  335.897656   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 1300.49845     1,511  .860687261   {txt}R-squared       ={res}    0.4366
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.4355
{txt}       Total {c |} {res} 2308.19142     1,514  1.52456501   {txt}Root MSE        =   {res} .92773

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         trust_gov{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}China_Commitment {c |}{col 20}{res}{space 2}-.1631451{col 32}{space 2}  .067573{col 43}{space 1}   -2.41{col 52}{space 3}0.016{col 60}{space 4}-.2956919{col 73}{space 3}-.0305983
{txt}Japan_NoCommitment {c |}{col 20}{res}{space 2} 1.612918{col 32}{space 2} .0672611{col 43}{space 1}   23.98{col 52}{space 3}0.000{col 60}{space 4} 1.480983{col 73}{space 3} 1.744853
{txt}{space 2}Japan_Commitment {c |}{col 20}{res}{space 2} 1.472296{col 32}{space 2} .0673935{col 43}{space 1}   21.85{col 52}{space 3}0.000{col 60}{space 4} 1.340101{col 73}{space 3}  1.60449
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} 1.955145{col 32}{space 2} .0476544{col 43}{space 1}   41.03{col 52}{space 3}0.000{col 60}{space 4} 1.861669{col 73}{space 3} 2.048621
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model3
{txt}
{com}. lincom _b[China_Commitment]+(_b[Japan_NoCommitment]-_b[Japan_Commitment])

{p 0 7}{space 1}{text:( 1)}{space 1} {res}China_Commitment + Japan_NoCommitment - Japan_Commitment = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   trust_gov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0225229{col 26}{space 2} .0953423{col 37}{space 1}   -0.24{col 46}{space 3}0.813{col 54}{space 4}-.2095403{col 67}{space 3} .1644944
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. reg trust_cit China_Commitment Japan_NoCommitment Japan_Commitment

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,515
{txt}{hline 13}{c +}{hline 34}   F(3, 1511)      = {res}   156.22
{txt}       Model {c |} {res} 383.821111         3   127.94037   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 1237.50166     1,511  .818995143   {txt}R-squared       ={res}    0.2367
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2352
{txt}       Total {c |} {res} 1621.32277     1,514   1.0708869   {txt}Root MSE        =   {res} .90498

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         trust_cit{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}China_Commitment {c |}{col 20}{res}{space 2}  -.11708{col 32}{space 2}  .065916{col 43}{space 1}   -1.78{col 52}{space 3}0.076{col 60}{space 4}-.2463767{col 73}{space 3} .0122166
{txt}Japan_NoCommitment {c |}{col 20}{res}{space 2} .9631643{col 32}{space 2} .0656118{col 43}{space 1}   14.68{col 52}{space 3}0.000{col 60}{space 4} .8344645{col 73}{space 3} 1.091864
{txt}{space 2}Japan_Commitment {c |}{col 20}{res}{space 2} .9261214{col 32}{space 2}  .065741{col 43}{space 1}   14.09{col 52}{space 3}0.000{col 60}{space 4} .7971682{col 73}{space 3} 1.055075
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} 3.023747{col 32}{space 2} .0464859{col 43}{space 1}   65.05{col 52}{space 3}0.000{col 60}{space 4} 2.932563{col 73}{space 3}  3.11493
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model4
{txt}
{com}. lincom _b[China_Commitment]+(_b[Japan_NoCommitment]-_b[Japan_Commitment])

{p 0 7}{space 1}{text:( 1)}{space 1} {res}China_Commitment + Japan_NoCommitment - Japan_Commitment = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   trust_cit{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0800371{col 26}{space 2} .0930044{col 37}{space 1}   -0.86{col 46}{space 3}0.390{col 54}{space 4}-.2624686{col 67}{space 3} .1023944
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. esttab Model1 Model2 Model3 Model4 using conditional_effect.tex, replace se r2 legend label collabels(none) varlabels(_cons Constant) star(* 0.05 ** 0.01)
{res}{txt}(output written to {browse  `"conditional_effect.tex"'})

{com}. 
. **Table A1: Descriptive Statistics
. sum i.condition credibility support collaborate trust_gov trust_cit Male Age White Ideology Democrat Republican Voting_Pres20 Income Education Feeling_China Feeling_Japan Knowledge_China Knowledge_Japan

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}condition {c |}
China/No ..  {c |}{res}      1,515     .250165    .4332509          0          1
{txt}China/Com~t  {c |}{res}      1,515    .2475248    .4317167          0          1
{txt}Japan/No ..  {c |}{res}      1,515    .2521452    .4343876          0          1
{txt}Japan/Com~t  {c |}{res}      1,515     .250165    .4332509          0          1
{txt}{space 12} {c |}
{space 1}credibility {c |}{res}      1,507    2.453218    .9188349          1          4
{txt}{hline 13}{c +}{hline 57}
{space 5}support {c |}{res}      1,515    2.685149    1.316699          1          5
{txt}{space 1}collaborate {c |}{res}      1,515    3.674587    1.155817          1          5
{txt}{space 3}trust_gov {c |}{res}      1,515    2.689769    1.234733          1          5
{txt}{space 3}trust_cit {c |}{res}      1,515    3.469307    1.034837          1          5
{txt}{space 8}Male {c |}{res}      1,494    .5060241    .5001311          0          1
{txt}{hline 13}{c +}{hline 57}
{space 9}Age {c |}{res}      1,515    3.183498    1.294993          1          6
{txt}{space 7}White {c |}{res}      1,507    .6834771    .4652739          0          1
{txt}{space 4}Ideology {c |}{res}      1,509    4.245858    1.817938          1          7
{txt}{space 4}Democrat {c |}{res}      1,511     .347452    .4763184          0          1
{txt}{space 2}Republican {c |}{res}      1,511    .3163468     .465204          0          1
{txt}{hline 13}{c +}{hline 57}
Voting_Pr~20 {c |}{res}      1,504    .8570479    .3501404          0          1
{txt}{space 6}Income {c |}{res}      1,490    4.083221    2.190964          1          9
{txt}{space 3}Education {c |}{res}      1,511    .5890139    .4921756          0          1
{txt}Feeling_Ch~a {c |}{res}      1,512    3.354497    1.466139          1          7
{txt}Feeling_Ja~n {c |}{res}      1,514    5.471598    1.260325          1          7
{txt}{hline 13}{c +}{hline 57}
Knowledge_~a {c |}{res}      1,515    .7617162    .4261743          0          1
{txt}Knowledge_~n {c |}{res}      1,515    .3240924    .4681893          0          1
{txt}
{com}. 
. **Table A2: Balance Check
. ttesttable Male condition, ref(1) uneq

{txt}Cross-table of differences among groups with t-Test
{col 22}1
{hline 27}
1{res}{col 15}     0
{txt}2{res}{col 15}-0.031
{txt}3{res}{col 15} 0.000
{txt}4{res}{col 15} 0.007
{txt}{hline 27}
Note: Differences defined as column-line
      * p<.1; ** p<.05; *** p<.01
{hline 27}

{com}. ttesttable Age condition, ref(1) uneq

{txt}Cross-table of differences among groups with t-Test
{col 22}1
{hline 27}
1{res}{col 15}     0
{txt}2{res}{col 15}-0.047
{txt}3{res}{col 15}-0.048
{txt}4{res}{col 15} 0.005
{txt}{hline 27}
Note: Differences defined as column-line
      * p<.1; ** p<.05; *** p<.01
{hline 27}

{com}. ttesttable White condition, ref(1) uneq

{txt}Cross-table of differences among groups with t-Test
{col 22}1
{hline 27}
1{res}{col 15}     0
{txt}2{res}{col 15} 0.020
{txt}3{res}{col 15} 0.071**
{txt}4{res}{col 15} 0.026
{txt}{hline 27}
Note: Differences defined as column-line
      * p<.1; ** p<.05; *** p<.01
{hline 27}

{com}. ttesttable Democrat condition, ref(1) uneq

{txt}Cross-table of differences among groups with t-Test
{col 22}1
{hline 27}
1{res}{col 15}     0
{txt}2{res}{col 15}-0.027
{txt}3{res}{col 15}-0.080**
{txt}4{res}{col 15}-0.044
{txt}{hline 27}
Note: Differences defined as column-line
      * p<.1; ** p<.05; *** p<.01
{hline 27}

{com}. ttesttable Republican condition, ref(1) uneq

{txt}Cross-table of differences among groups with t-Test
{col 22}1
{hline 27}
1{res}{col 15}     0
{txt}2{res}{col 15} 0.039
{txt}3{res}{col 15} 0.081**
{txt}4{res}{col 15} 0.054
{txt}{hline 27}
Note: Differences defined as column-line
      * p<.1; ** p<.05; *** p<.01
{hline 27}

{com}. ttesttable Ideology condition, ref(1) uneq

{txt}Cross-table of differences among groups with t-Test
{col 22}1
{hline 27}
1{res}{col 15}     0
{txt}2{res}{col 15}-0.001
{txt}3{res}{col 15}-0.190
{txt}4{res}{col 15}-0.090
{txt}{hline 27}
Note: Differences defined as column-line
      * p<.1; ** p<.05; *** p<.01
{hline 27}

{com}. ttesttable Voting_Pres20 condition, ref(1) uneq

{txt}Cross-table of differences among groups with t-Test
{col 22}1
{hline 27}
1{res}{col 15}     0
{txt}2{res}{col 15} 0.024
{txt}3{res}{col 15} 0.010
{txt}4{res}{col 15} 0.029
{txt}{hline 27}
Note: Differences defined as column-line
      * p<.1; ** p<.05; *** p<.01
{hline 27}

{com}. ttesttable Income condition, ref(1) uneq

{txt}Cross-table of differences among groups with t-Test
{col 22}1
{hline 27}
1{res}{col 15}     0
{txt}2{res}{col 15}-0.011
{txt}3{res}{col 15}-0.073
{txt}4{res}{col 15}-0.132
{txt}{hline 27}
Note: Differences defined as column-line
      * p<.1; ** p<.05; *** p<.01
{hline 27}

{com}. ttesttable Education condition, ref(1) uneq

{txt}Cross-table of differences among groups with t-Test
{col 22}1
{hline 27}
1{res}{col 15}     0
{txt}2{res}{col 15}-0.019
{txt}3{res}{col 15}-0.054
{txt}4{res}{col 15}-0.039
{txt}{hline 27}
Note: Differences defined as column-line
      * p<.1; ** p<.05; *** p<.01
{hline 27}

{com}. ttesttable Feeling_China condition, ref(1) uneq

{txt}Cross-table of differences among groups with t-Test
{col 22}1
{hline 27}
1{res}{col 15}     0
{txt}2{res}{col 15} 0.214**
{txt}3{res}{col 15}-0.079
{txt}4{res}{col 15} 0.191*
{txt}{hline 27}
Note: Differences defined as column-line
      * p<.1; ** p<.05; *** p<.01
{hline 27}

{com}. ttesttable Feeling_Japan condition, ref(1) uneq

{txt}Cross-table of differences among groups with t-Test
{col 22}1
{hline 27}
1{res}{col 15}     0
{txt}2{res}{col 15} 0.040
{txt}3{res}{col 15}-0.062
{txt}4{res}{col 15}-0.007
{txt}{hline 27}
Note: Differences defined as column-line
      * p<.1; ** p<.05; *** p<.01
{hline 27}

{com}. ttesttable Knowledge_China condition, ref(1) uneq

{txt}Cross-table of differences among groups with t-Test
{col 22}1
{hline 27}
1{res}{col 15}     0
{txt}2{res}{col 15}-0.000
{txt}3{res}{col 15}-0.012
{txt}4{res}{col 15}-0.005
{txt}{hline 27}
Note: Differences defined as column-line
      * p<.1; ** p<.05; *** p<.01
{hline 27}

{com}. ttesttable Knowledge_Japan condition, ref(1) uneq

{txt}Cross-table of differences among groups with t-Test
{col 22}1
{hline 27}
1{res}{col 15}     0
{txt}2{res}{col 15}-0.019
{txt}3{res}{col 15} 0.018
{txt}4{res}{col 15}-0.008
{txt}{hline 27}
Note: Differences defined as column-line
      * p<.1; ** p<.05; *** p<.01
{hline 27}

{com}. 
. **Table A3: Controlling for Demographic and Attitudinal Variables
. reg credibility i.condition Male Age White Ideology Democrat Republican Income Voting_Pres20 Education Feeling_China Feeling_Japan Knowledge_China Knowledge_Japan if condition==1 | condition==2

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       718
{txt}{hline 13}{c +}{hline 34}   F(14, 703)      = {res}    17.47
{txt}       Model {c |} {res} 119.750813        14   8.5536295   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 344.182335       703  .489590803   {txt}R-squared       ={res}    0.2581
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2433
{txt}       Total {c |} {res} 463.933148       717  .647047626   {txt}Root MSE        =   {res} .69971

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.3432959{col 31}{space 2} .0527732{col 42}{space 1}   -6.51{col 51}{space 3}0.000{col 59}{space 4}-.4469078{col 72}{space 3}-.2396839
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .0973636{col 31}{space 2} .0557772{col 42}{space 1}    1.75{col 51}{space 3}0.081{col 59}{space 4}-.0121462{col 72}{space 3} .2068734
{txt}{space 14}Age {c |}{col 19}{res}{space 2}-.0554938{col 31}{space 2} .0212806{col 42}{space 1}   -2.61{col 51}{space 3}0.009{col 59}{space 4} -.097275{col 72}{space 3}-.0137127
{txt}{space 12}White {c |}{col 19}{res}{space 2} -.069214{col 31}{space 2}  .060346{col 42}{space 1}   -1.15{col 51}{space 3}0.252{col 59}{space 4}-.1876939{col 72}{space 3} .0492659
{txt}{space 9}Ideology {c |}{col 19}{res}{space 2} .0422817{col 31}{space 2} .0244771{col 42}{space 1}    1.73{col 51}{space 3}0.085{col 59}{space 4}-.0057752{col 72}{space 3} .0903387
{txt}{space 9}Democrat {c |}{col 19}{res}{space 2}-.1365891{col 31}{space 2} .0751043{col 42}{space 1}   -1.82{col 51}{space 3}0.069{col 59}{space 4}-.2840447{col 72}{space 3} .0108665
{txt}{space 7}Republican {c |}{col 19}{res}{space 2} .0981927{col 31}{space 2} .0829987{col 42}{space 1}    1.18{col 51}{space 3}0.237{col 59}{space 4}-.0647624{col 72}{space 3} .2611478
{txt}{space 11}Income {c |}{col 19}{res}{space 2}-.0037493{col 31}{space 2} .0131075{col 42}{space 1}   -0.29{col 51}{space 3}0.775{col 59}{space 4} -.029484{col 72}{space 3} .0219853
{txt}{space 4}Voting_Pres20 {c |}{col 19}{res}{space 2} .0257195{col 31}{space 2} .0801756{col 42}{space 1}    0.32{col 51}{space 3}0.748{col 59}{space 4}-.1316928{col 72}{space 3} .1831318
{txt}{space 8}Education {c |}{col 19}{res}{space 2} .1068409{col 31}{space 2} .0586978{col 42}{space 1}    1.82{col 51}{space 3}0.069{col 59}{space 4} -.008403{col 72}{space 3} .2220848
{txt}{space 4}Feeling_China {c |}{col 19}{res}{space 2} .2122773{col 31}{space 2} .0197365{col 42}{space 1}   10.76{col 51}{space 3}0.000{col 59}{space 4} .1735277{col 72}{space 3} .2510268
{txt}{space 4}Feeling_Japan {c |}{col 19}{res}{space 2}-.0732858{col 31}{space 2}  .020941{col 42}{space 1}   -3.50{col 51}{space 3}0.000{col 59}{space 4}-.1144002{col 72}{space 3}-.0321714
{txt}{space 2}Knowledge_China {c |}{col 19}{res}{space 2}-.0943687{col 31}{space 2} .0695359{col 42}{space 1}   -1.36{col 51}{space 3}0.175{col 59}{space 4}-.2308917{col 72}{space 3} .0421542
{txt}{space 2}Knowledge_Japan {c |}{col 19}{res}{space 2} .0620194{col 31}{space 2} .0610852{col 42}{space 1}    1.02{col 51}{space 3}0.310{col 59}{space 4}-.0579118{col 72}{space 3} .1819506
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.764264{col 31}{space 2} .2014398{col 42}{space 1}    8.76{col 51}{space 3}0.000{col 59}{space 4} 1.368769{col 72}{space 3}  2.15976
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model1
{txt}
{com}. 
. reg credibility_dummy i.condition Male Age White Ideology Democrat Republican Income Voting_Pres20 Education Feeling_China Feeling_Japan Knowledge_China Knowledge_Japan if condition==1 | condition==2

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       718
{txt}{hline 13}{c +}{hline 34}   F(14, 703)      = {res}     7.99
{txt}       Model {c |} {res} 17.0713349        14  1.21938107   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 107.274069       703  .152594693   {txt}R-squared       ={res}    0.1373
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1201
{txt}       Total {c |} {res} 124.345404       717  .173424552   {txt}Root MSE        =   {res} .39063

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}credibility_dummy{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.1608235{col 31}{space 2} .0294623{col 42}{space 1}   -5.46{col 51}{space 3}0.000{col 59}{space 4}-.2186682{col 72}{space 3}-.1029789
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .0623672{col 31}{space 2} .0311393{col 42}{space 1}    2.00{col 51}{space 3}0.046{col 59}{space 4}   .00123{col 72}{space 3} .1235045
{txt}{space 14}Age {c |}{col 19}{res}{space 2}-.0264784{col 31}{space 2} .0118806{col 42}{space 1}   -2.23{col 51}{space 3}0.026{col 59}{space 4} -.049804{col 72}{space 3}-.0031527
{txt}{space 12}White {c |}{col 19}{res}{space 2}-.0466246{col 31}{space 2}   .03369{col 42}{space 1}   -1.38{col 51}{space 3}0.167{col 59}{space 4}-.1127697{col 72}{space 3} .0195205
{txt}{space 9}Ideology {c |}{col 19}{res}{space 2}-.0062526{col 31}{space 2} .0136651{col 42}{space 1}   -0.46{col 51}{space 3}0.647{col 59}{space 4}-.0330819{col 72}{space 3} .0205767
{txt}{space 9}Democrat {c |}{col 19}{res}{space 2}-.0068242{col 31}{space 2} .0419293{col 42}{space 1}   -0.16{col 51}{space 3}0.871{col 59}{space 4}-.0891459{col 72}{space 3} .0754975
{txt}{space 7}Republican {c |}{col 19}{res}{space 2} .0278442{col 31}{space 2} .0463366{col 42}{space 1}    0.60{col 51}{space 3}0.548{col 59}{space 4}-.0631306{col 72}{space 3} .1188189
{txt}{space 11}Income {c |}{col 19}{res}{space 2}-.0054738{col 31}{space 2} .0073177{col 42}{space 1}   -0.75{col 51}{space 3}0.455{col 59}{space 4} -.019841{col 72}{space 3} .0088933
{txt}{space 4}Voting_Pres20 {c |}{col 19}{res}{space 2} .0413573{col 31}{space 2} .0447605{col 42}{space 1}    0.92{col 51}{space 3}0.356{col 59}{space 4}-.0465231{col 72}{space 3} .1292376
{txt}{space 8}Education {c |}{col 19}{res}{space 2} .0288391{col 31}{space 2} .0327699{col 42}{space 1}    0.88{col 51}{space 3}0.379{col 59}{space 4}-.0354995{col 72}{space 3} .0931776
{txt}{space 4}Feeling_China {c |}{col 19}{res}{space 2}  .067779{col 31}{space 2} .0110185{col 42}{space 1}    6.15{col 51}{space 3}0.000{col 59}{space 4} .0461458{col 72}{space 3} .0894121
{txt}{space 4}Feeling_Japan {c |}{col 19}{res}{space 2}-.0284465{col 31}{space 2}  .011691{col 42}{space 1}   -2.43{col 51}{space 3}0.015{col 59}{space 4}-.0513999{col 72}{space 3}-.0054931
{txt}{space 2}Knowledge_China {c |}{col 19}{res}{space 2}-.0456671{col 31}{space 2} .0388206{col 42}{space 1}   -1.18{col 51}{space 3}0.240{col 59}{space 4}-.1218853{col 72}{space 3} .0305512
{txt}{space 2}Knowledge_Japan {c |}{col 19}{res}{space 2} .0537083{col 31}{space 2} .0341027{col 42}{space 1}    1.57{col 51}{space 3}0.116{col 59}{space 4}-.0132471{col 72}{space 3} .1206636
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .3221436{col 31}{space 2} .1124601{col 42}{space 1}    2.86{col 51}{space 3}0.004{col 59}{space 4} .1013458{col 72}{space 3} .5429414
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model2
{txt}
{com}. 
. reg credibility i.condition Male Age White Ideology Democrat Republican Income Voting_Pres20 Education Feeling_China Feeling_Japan Knowledge_China Knowledge_Japan if condition==3 | condition==4

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       721
{txt}{hline 13}{c +}{hline 34}   F(14, 706)      = {res}    10.17
{txt}       Model {c |} {res} 56.1921184        14  4.01372274   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}  278.73992       706  .394815751   {txt}R-squared       ={res}    0.1678
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1513
{txt}       Total {c |} {res} 334.932039       720  .465183387   {txt}Root MSE        =   {res} .62834

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2} -.162096{col 31}{space 2} .0473301{col 42}{space 1}   -3.42{col 51}{space 3}0.001{col 59}{space 4}-.2550206{col 72}{space 3}-.0691715
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .0850664{col 31}{space 2} .0496135{col 42}{space 1}    1.71{col 51}{space 3}0.087{col 59}{space 4}-.0123413{col 72}{space 3}  .182474
{txt}{space 14}Age {c |}{col 19}{res}{space 2}  .014594{col 31}{space 2} .0194799{col 42}{space 1}    0.75{col 51}{space 3}0.454{col 59}{space 4}-.0236514{col 72}{space 3} .0528395
{txt}{space 12}White {c |}{col 19}{res}{space 2} .0660107{col 31}{space 2} .0529809{col 42}{space 1}    1.25{col 51}{space 3}0.213{col 59}{space 4}-.0380083{col 72}{space 3} .1700298
{txt}{space 9}Ideology {c |}{col 19}{res}{space 2}-.0277938{col 31}{space 2} .0223593{col 42}{space 1}   -1.24{col 51}{space 3}0.214{col 59}{space 4}-.0716924{col 72}{space 3} .0161049
{txt}{space 9}Democrat {c |}{col 19}{res}{space 2} -.013867{col 31}{space 2} .0649496{col 42}{space 1}   -0.21{col 51}{space 3}0.831{col 59}{space 4}-.1413845{col 72}{space 3} .1136505
{txt}{space 7}Republican {c |}{col 19}{res}{space 2}-.0877311{col 31}{space 2} .0749915{col 42}{space 1}   -1.17{col 51}{space 3}0.242{col 59}{space 4}-.2349642{col 72}{space 3} .0595021
{txt}{space 11}Income {c |}{col 19}{res}{space 2}-.0040828{col 31}{space 2} .0116084{col 42}{space 1}   -0.35{col 51}{space 3}0.725{col 59}{space 4}-.0268739{col 72}{space 3} .0187083
{txt}{space 4}Voting_Pres20 {c |}{col 19}{res}{space 2}-.0109091{col 31}{space 2} .0698454{col 42}{space 1}   -0.16{col 51}{space 3}0.876{col 59}{space 4}-.1480387{col 72}{space 3} .1262204
{txt}{space 8}Education {c |}{col 19}{res}{space 2} -.056542{col 31}{space 2} .0527175{col 42}{space 1}   -1.07{col 51}{space 3}0.284{col 59}{space 4} -.160044{col 72}{space 3} .0469599
{txt}{space 4}Feeling_China {c |}{col 19}{res}{space 2} -.050641{col 31}{space 2} .0169586{col 42}{space 1}   -2.99{col 51}{space 3}0.003{col 59}{space 4}-.0839363{col 72}{space 3}-.0173457
{txt}{space 4}Feeling_Japan {c |}{col 19}{res}{space 2}  .193189{col 31}{space 2} .0200695{col 42}{space 1}    9.63{col 51}{space 3}0.000{col 59}{space 4} .1537859{col 72}{space 3}  .232592
{txt}{space 2}Knowledge_China {c |}{col 19}{res}{space 2}-.0333144{col 31}{space 2} .0626773{col 42}{space 1}   -0.53{col 51}{space 3}0.595{col 59}{space 4}-.1563706{col 72}{space 3} .0897417
{txt}{space 2}Knowledge_Japan {c |}{col 19}{res}{space 2} .0533506{col 31}{space 2} .0550612{col 42}{space 1}    0.97{col 51}{space 3}0.333{col 59}{space 4}-.0547528{col 72}{space 3}  .161454
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.267298{col 31}{space 2} .1929297{col 42}{space 1}   11.75{col 51}{space 3}0.000{col 59}{space 4} 1.888514{col 72}{space 3} 2.646083
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model3
{txt}
{com}. 
. reg credibility_dummy i.condition Male Age White Ideology Democrat Republican Income Voting_Pres20 Education Feeling_China Feeling_Japan Knowledge_China Knowledge_Japan if condition==3 | condition==4

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       721
{txt}{hline 13}{c +}{hline 34}   F(14, 706)      = {res}     4.77
{txt}       Model {c |} {res} 9.48184344        14  .677274531   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 100.240764       706  .141984085   {txt}R-squared       ={res}    0.0864
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0683
{txt}       Total {c |} {res} 109.722607       720   .15239251   {txt}Root MSE        =   {res} .37681

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}credibility_dummy{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.0495651{col 31}{space 2} .0283831{col 42}{space 1}   -1.75{col 51}{space 3}0.081{col 59}{space 4}-.1052905{col 72}{space 3} .0061603
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .0308901{col 31}{space 2} .0297524{col 42}{space 1}    1.04{col 51}{space 3}0.300{col 59}{space 4}-.0275238{col 72}{space 3} .0893039
{txt}{space 14}Age {c |}{col 19}{res}{space 2} .0012461{col 31}{space 2} .0116818{col 42}{space 1}    0.11{col 51}{space 3}0.915{col 59}{space 4}-.0216891{col 72}{space 3} .0241813
{txt}{space 12}White {c |}{col 19}{res}{space 2} .0509859{col 31}{space 2} .0317718{col 42}{space 1}    1.60{col 51}{space 3}0.109{col 59}{space 4}-.0113927{col 72}{space 3} .1133644
{txt}{space 9}Ideology {c |}{col 19}{res}{space 2}-.0069934{col 31}{space 2} .0134085{col 42}{space 1}   -0.52{col 51}{space 3}0.602{col 59}{space 4}-.0333188{col 72}{space 3} .0193319
{txt}{space 9}Democrat {c |}{col 19}{res}{space 2} -.020561{col 31}{space 2} .0389493{col 42}{space 1}   -0.53{col 51}{space 3}0.598{col 59}{space 4}-.0970313{col 72}{space 3} .0559092
{txt}{space 7}Republican {c |}{col 19}{res}{space 2}-.0356685{col 31}{space 2} .0449712{col 42}{space 1}   -0.79{col 51}{space 3}0.428{col 59}{space 4}-.1239619{col 72}{space 3} .0526249
{txt}{space 11}Income {c |}{col 19}{res}{space 2}-.0025976{col 31}{space 2} .0069614{col 42}{space 1}   -0.37{col 51}{space 3}0.709{col 59}{space 4} -.016265{col 72}{space 3} .0110699
{txt}{space 4}Voting_Pres20 {c |}{col 19}{res}{space 2} .0086396{col 31}{space 2} .0418852{col 42}{space 1}    0.21{col 51}{space 3}0.837{col 59}{space 4}-.0735949{col 72}{space 3}  .090874
{txt}{space 8}Education {c |}{col 19}{res}{space 2}-.0207206{col 31}{space 2} .0316139{col 42}{space 1}   -0.66{col 51}{space 3}0.512{col 59}{space 4}-.0827891{col 72}{space 3} .0413478
{txt}{space 4}Feeling_China {c |}{col 19}{res}{space 2}-.0212228{col 31}{space 2} .0101698{col 42}{space 1}   -2.09{col 51}{space 3}0.037{col 59}{space 4}-.0411894{col 72}{space 3}-.0012561
{txt}{space 4}Feeling_Japan {c |}{col 19}{res}{space 2} .0822531{col 31}{space 2} .0120354{col 42}{space 1}    6.83{col 51}{space 3}0.000{col 59}{space 4} .0586238{col 72}{space 3} .1058825
{txt}{space 2}Knowledge_China {c |}{col 19}{res}{space 2}-.0400328{col 31}{space 2} .0375866{col 42}{space 1}   -1.07{col 51}{space 3}0.287{col 59}{space 4}-.1138277{col 72}{space 3}  .033762
{txt}{space 2}Knowledge_Japan {c |}{col 19}{res}{space 2} .0312228{col 31}{space 2} .0330194{col 42}{space 1}    0.95{col 51}{space 3}0.345{col 59}{space 4}-.0336051{col 72}{space 3} .0960507
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .4889692{col 31}{space 2} .1156969{col 42}{space 1}    4.23{col 51}{space 3}0.000{col 59}{space 4}  .261818{col 72}{space 3} .7161203
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model4
{txt}
{com}. 
. esttab Model1 Model2 Model3 Model4 using covariants.tex, replace se r2 legend label collabels(none) varlabels(_cons Constant) star(* 0.05 ** 0.01)
{res}{txt}(output written to {browse  `"covariants.tex"'})

{com}. 
. **Tables A6 and A7: Exclude inattentive subjects
. tab attention

  {txt}Attention {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         No {c |}{res}      1,515      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,515      100.00
{txt}
{com}. sum durationinseconds, detail

                    {txt}Duration (in seconds)
{hline 61}
      Percentiles      Smallest
 1%    {res}       84             60
{txt} 5%    {res}      103             64
{txt}10%    {res}      121             67       {txt}Obs         {res}      1,515
{txt}25%    {res}      154             69       {txt}Sum of Wgt. {res}      1,515

{txt}50%    {res}      204                      {txt}Mean          {res} 281.1168
                        {txt}Largest       Std. Dev.     {res} 373.0114
{txt}75%    {res}      301           2214
{txt}90%    {res}      472           2235       {txt}Variance      {res} 139137.5
{txt}95%    {res}      669           2541       {txt}Skewness      {res} 18.82064
{txt}99%    {res}     1365          11452       {txt}Kurtosis      {res} 536.2174
{txt}
{com}. g inattentive=0
{txt}
{com}. replace inattentive=1 if durationinseconds<103 | durationinseconds>669
{txt}(146 real changes made)

{com}. tab inattentive

{txt}inattentive {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,369       90.36       90.36
{txt}          1 {c |}{res}        146        9.64      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,515      100.00
{txt}
{com}. 
. *Continous
. reg credibility i.condition if inattentive==0 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       685
{txt}{hline 13}{c +}{hline 34}   F(1, 683)       = {res}    44.68
{txt}       Model {c |} {res} 27.0432705         1  27.0432705   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 413.388846       683  .605254533   {txt}R-squared       ={res}    0.0614
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0600
{txt}       Total {c |} {res} 440.432117       684  .643906603   {txt}Root MSE        =   {res} .77798

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2} -.397388{col 31}{space 2} .0594503{col 42}{space 1}   -6.68{col 51}{space 3}0.000{col 59}{space 4}-.5141154{col 72}{space 3}-.2806607
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.093294{col 31}{space 2}  .042007{col 42}{space 1}   49.83{col 51}{space 3}0.000{col 59}{space 4} 2.010816{col 72}{space 3} 2.175773
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model1
{txt}
{com}. 
. reg credibility i.condition Male Age White Ideology Democrat Republican Income Voting_Pres20 Education Feeling_China Feeling_Japan Knowledge_China Knowledge_Japan if inattentive==0 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       659
{txt}{hline 13}{c +}{hline 34}   F(14, 644)      = {res}    16.83
{txt}       Model {c |} {res} 113.914627        14  8.13675906   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 311.435904       644  .483596125   {txt}R-squared       ={res}    0.2678
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2519
{txt}       Total {c |} {res} 425.350531       658  .646429379   {txt}Root MSE        =   {res} .69541

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.3377843{col 31}{space 2} .0546434{col 42}{space 1}   -6.18{col 51}{space 3}0.000{col 59}{space 4} -.445085{col 72}{space 3}-.2304835
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .0855269{col 31}{space 2} .0577683{col 42}{space 1}    1.48{col 51}{space 3}0.139{col 59}{space 4}  -.02791{col 72}{space 3} .1989638
{txt}{space 14}Age {c |}{col 19}{res}{space 2}-.0540884{col 31}{space 2}  .021967{col 42}{space 1}   -2.46{col 51}{space 3}0.014{col 59}{space 4} -.097224{col 72}{space 3}-.0109529
{txt}{space 12}White {c |}{col 19}{res}{space 2}-.0938686{col 31}{space 2} .0631395{col 42}{space 1}   -1.49{col 51}{space 3}0.138{col 59}{space 4}-.2178527{col 72}{space 3} .0301155
{txt}{space 9}Ideology {c |}{col 19}{res}{space 2} .0529855{col 31}{space 2} .0251868{col 42}{space 1}    2.10{col 51}{space 3}0.036{col 59}{space 4} .0035274{col 72}{space 3} .1024436
{txt}{space 9}Democrat {c |}{col 19}{res}{space 2}  -.15174{col 31}{space 2} .0777339{col 42}{space 1}   -1.95{col 51}{space 3}0.051{col 59}{space 4}-.3043825{col 72}{space 3} .0009025
{txt}{space 7}Republican {c |}{col 19}{res}{space 2} .1344089{col 31}{space 2} .0853511{col 42}{space 1}    1.57{col 51}{space 3}0.116{col 59}{space 4}-.0331911{col 72}{space 3} .3020089
{txt}{space 11}Income {c |}{col 19}{res}{space 2}-.0045616{col 31}{space 2} .0135642{col 42}{space 1}   -0.34{col 51}{space 3}0.737{col 59}{space 4} -.031197{col 72}{space 3} .0220739
{txt}{space 4}Voting_Pres20 {c |}{col 19}{res}{space 2}-.0060916{col 31}{space 2} .0825067{col 42}{space 1}   -0.07{col 51}{space 3}0.941{col 59}{space 4}-.1681063{col 72}{space 3} .1559231
{txt}{space 8}Education {c |}{col 19}{res}{space 2} .1103109{col 31}{space 2} .0606496{col 42}{space 1}    1.82{col 51}{space 3}0.069{col 59}{space 4} -.008784{col 72}{space 3} .2294058
{txt}{space 4}Feeling_China {c |}{col 19}{res}{space 2} .2139492{col 31}{space 2} .0205109{col 42}{space 1}   10.43{col 51}{space 3}0.000{col 59}{space 4} .1736729{col 72}{space 3} .2542255
{txt}{space 4}Feeling_Japan {c |}{col 19}{res}{space 2}-.0817925{col 31}{space 2} .0218517{col 42}{space 1}   -3.74{col 51}{space 3}0.000{col 59}{space 4}-.1247018{col 72}{space 3}-.0388833
{txt}{space 2}Knowledge_China {c |}{col 19}{res}{space 2}-.0642128{col 31}{space 2} .0714667{col 42}{space 1}   -0.90{col 51}{space 3}0.369{col 59}{space 4}-.2045487{col 72}{space 3} .0761231
{txt}{space 2}Knowledge_Japan {c |}{col 19}{res}{space 2} .0565253{col 31}{space 2} .0633874{col 42}{space 1}    0.89{col 51}{space 3}0.373{col 59}{space 4}-.0679457{col 72}{space 3} .1809963
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}  1.77785{col 31}{space 2} .2070337{col 42}{space 1}    8.59{col 51}{space 3}0.000{col 59}{space 4} 1.371307{col 72}{space 3} 2.184393
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model2
{txt}
{com}. 
. reg credibility i.condition if inattentive==0 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       677
{txt}{hline 13}{c +}{hline 34}   F(1, 675)       = {res}     6.29
{txt}       Model {c |} {res} 2.86367256         1  2.86367256   {txt}Prob > F        ={res}    0.0123
{txt}    Residual {c |} {res} 307.112694       675  .454981769   {txt}R-squared       ={res}    0.0092
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0078
{txt}       Total {c |} {res} 309.976366       676  .458544921   {txt}Root MSE        =   {res} .67452

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.1300796{col 31}{space 2} .0518495{col 42}{space 1}   -2.51{col 51}{space 3}0.012{col 59}{space 4}-.2318853{col 72}{space 3}-.0282739
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.058651{col 31}{space 2} .0365275{col 42}{space 1}   83.74{col 51}{space 3}0.000{col 59}{space 4}  2.98693{col 72}{space 3} 3.130372
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model3
{txt}
{com}. 
. reg credibility i.condition Male Age White Ideology Democrat Republican Income Voting_Pres20 Education Feeling_China Feeling_Japan Knowledge_China Knowledge_Japan if inattentive==0 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       640
{txt}{hline 13}{c +}{hline 34}   F(14, 625)      = {res}     8.73
{txt}       Model {c |} {res} 49.0534568        14  3.50381834   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 250.890293       625  .401424469   {txt}R-squared       ={res}    0.1635
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1448
{txt}       Total {c |} {res}  299.94375       639   .46939554   {txt}Root MSE        =   {res} .63358

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.1381846{col 31}{space 2} .0507334{col 42}{space 1}   -2.72{col 51}{space 3}0.007{col 59}{space 4}-.2378132{col 72}{space 3}-.0385559
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .0793583{col 31}{space 2} .0530979{col 42}{space 1}    1.49{col 51}{space 3}0.136{col 59}{space 4}-.0249135{col 72}{space 3} .1836301
{txt}{space 14}Age {c |}{col 19}{res}{space 2} .0131766{col 31}{space 2} .0207657{col 42}{space 1}    0.63{col 51}{space 3}0.526{col 59}{space 4}-.0276023{col 72}{space 3} .0539555
{txt}{space 12}White {c |}{col 19}{res}{space 2} .0733472{col 31}{space 2} .0569295{col 42}{space 1}    1.29{col 51}{space 3}0.198{col 59}{space 4} -.038449{col 72}{space 3} .1851434
{txt}{space 9}Ideology {c |}{col 19}{res}{space 2}-.0315592{col 31}{space 2} .0241704{col 42}{space 1}   -1.31{col 51}{space 3}0.192{col 59}{space 4}-.0790242{col 72}{space 3} .0159057
{txt}{space 9}Democrat {c |}{col 19}{res}{space 2}-.0185679{col 31}{space 2} .0691563{col 42}{space 1}   -0.27{col 51}{space 3}0.788{col 59}{space 4}-.1543748{col 72}{space 3} .1172389
{txt}{space 7}Republican {c |}{col 19}{res}{space 2}-.0757052{col 31}{space 2} .0800086{col 42}{space 1}   -0.95{col 51}{space 3}0.344{col 59}{space 4}-.2328234{col 72}{space 3}  .081413
{txt}{space 11}Income {c |}{col 19}{res}{space 2}   .00116{col 31}{space 2} .0124238{col 42}{space 1}    0.09{col 51}{space 3}0.926{col 59}{space 4}-.0232374{col 72}{space 3} .0255574
{txt}{space 4}Voting_Pres20 {c |}{col 19}{res}{space 2}  .002517{col 31}{space 2} .0750894{col 42}{space 1}    0.03{col 51}{space 3}0.973{col 59}{space 4}-.1449411{col 72}{space 3} .1499751
{txt}{space 8}Education {c |}{col 19}{res}{space 2}-.0689979{col 31}{space 2} .0561163{col 42}{space 1}   -1.23{col 51}{space 3}0.219{col 59}{space 4}-.1791972{col 72}{space 3} .0412014
{txt}{space 4}Feeling_China {c |}{col 19}{res}{space 2}-.0672574{col 31}{space 2}   .01817{col 42}{space 1}   -3.70{col 51}{space 3}0.000{col 59}{space 4} -.102939{col 72}{space 3}-.0315759
{txt}{space 4}Feeling_Japan {c |}{col 19}{res}{space 2} .1848537{col 31}{space 2} .0214339{col 42}{space 1}    8.62{col 51}{space 3}0.000{col 59}{space 4} .1427626{col 72}{space 3} .2269449
{txt}{space 2}Knowledge_China {c |}{col 19}{res}{space 2}-.0185486{col 31}{space 2} .0666386{col 42}{space 1}   -0.28{col 51}{space 3}0.781{col 59}{space 4}-.1494112{col 72}{space 3}  .112314
{txt}{space 2}Knowledge_Japan {c |}{col 19}{res}{space 2} .0156496{col 31}{space 2} .0586736{col 42}{space 1}    0.27{col 51}{space 3}0.790{col 59}{space 4}-.0995716{col 72}{space 3} .1308708
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.346359{col 31}{space 2} .2076571{col 42}{space 1}   11.30{col 51}{space 3}0.000{col 59}{space 4} 1.938569{col 72}{space 3} 2.754149
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model4
{txt}
{com}. 
. esttab Model1 Model2 Model3 Model4 using inattentive_continous.tex, replace se r2 legend label collabels(none) varlabels(_cons Constant) star(* 0.05 ** 0.01)
{res}{txt}(output written to {browse  `"inattentive_continous.tex"'})

{com}. 
. *Dichotomous
. reg credibility_dummy i.condition if inattentive==0 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       685
{txt}{hline 13}{c +}{hline 34}   F(1, 683)       = {res}    32.16
{txt}       Model {c |} {res} 5.21770407         1  5.21770407   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 110.805654       683  .162233753   {txt}R-squared       ={res}    0.0450
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0436
{txt}       Total {c |} {res} 116.023358       684  .169624792   {txt}Root MSE        =   {res} .40278

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}credibility_dummy{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2} -.174552{col 31}{space 2} .0307791{col 42}{space 1}   -5.67{col 51}{space 3}0.000{col 59}{space 4} -.234985{col 72}{space 3} -.114119
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}  .303207{col 31}{space 2} .0217482{col 42}{space 1}   13.94{col 51}{space 3}0.000{col 59}{space 4} .2605056{col 72}{space 3} .3459084
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model5
{txt}
{com}. 
. reg credibility_dummy i.condition Male Age White Ideology Democrat Republican Income Voting_Pres20 Education Feeling_China Feeling_Japan Knowledge_China Knowledge_Japan if inattentive==0 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       659
{txt}{hline 13}{c +}{hline 34}   F(14, 644)      = {res}     7.23
{txt}       Model {c |} {res} 15.2100429        14  1.08643163   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 96.7596081       644  .150247839   {txt}R-squared       ={res}    0.1358
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1171
{txt}       Total {c |} {res} 111.969651       658  .170166643   {txt}Root MSE        =   {res} .38762

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}credibility_dummy{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.1544575{col 31}{space 2} .0304579{col 42}{space 1}   -5.07{col 51}{space 3}0.000{col 59}{space 4}-.2142664{col 72}{space 3}-.0946487
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .0543635{col 31}{space 2} .0321997{col 42}{space 1}    1.69{col 51}{space 3}0.092{col 59}{space 4}-.0088656{col 72}{space 3} .1175926
{txt}{space 14}Age {c |}{col 19}{res}{space 2} -.026783{col 31}{space 2} .0122443{col 42}{space 1}   -2.19{col 51}{space 3}0.029{col 59}{space 4}-.0508265{col 72}{space 3}-.0027395
{txt}{space 12}White {c |}{col 19}{res}{space 2}-.0544106{col 31}{space 2} .0351936{col 42}{space 1}   -1.55{col 51}{space 3}0.123{col 59}{space 4}-.1235187{col 72}{space 3} .0146974
{txt}{space 9}Ideology {c |}{col 19}{res}{space 2}-.0003587{col 31}{space 2}  .014039{col 42}{space 1}   -0.03{col 51}{space 3}0.980{col 59}{space 4}-.0279264{col 72}{space 3}  .027209
{txt}{space 9}Democrat {c |}{col 19}{res}{space 2}-.0158061{col 31}{space 2} .0433284{col 42}{space 1}   -0.36{col 51}{space 3}0.715{col 59}{space 4}-.1008882{col 72}{space 3}  .069276
{txt}{space 7}Republican {c |}{col 19}{res}{space 2}  .039043{col 31}{space 2} .0475742{col 42}{space 1}    0.82{col 51}{space 3}0.412{col 59}{space 4}-.0543764{col 72}{space 3} .1324623
{txt}{space 11}Income {c |}{col 19}{res}{space 2}-.0035649{col 31}{space 2} .0075606{col 42}{space 1}   -0.47{col 51}{space 3}0.637{col 59}{space 4}-.0184114{col 72}{space 3} .0112815
{txt}{space 4}Voting_Pres20 {c |}{col 19}{res}{space 2} .0259059{col 31}{space 2} .0459888{col 42}{space 1}    0.56{col 51}{space 3}0.573{col 59}{space 4}-.0644002{col 72}{space 3} .1162121
{txt}{space 8}Education {c |}{col 19}{res}{space 2} .0197479{col 31}{space 2} .0338058{col 42}{space 1}    0.58{col 51}{space 3}0.559{col 59}{space 4}-.0466349{col 72}{space 3} .0861308
{txt}{space 4}Feeling_China {c |}{col 19}{res}{space 2} .0655317{col 31}{space 2} .0114327{col 42}{space 1}    5.73{col 51}{space 3}0.000{col 59}{space 4} .0430819{col 72}{space 3} .0879815
{txt}{space 4}Feeling_Japan {c |}{col 19}{res}{space 2}-.0319362{col 31}{space 2}   .01218{col 42}{space 1}   -2.62{col 51}{space 3}0.009{col 59}{space 4}-.0558536{col 72}{space 3}-.0080189
{txt}{space 2}Knowledge_China {c |}{col 19}{res}{space 2}-.0294908{col 31}{space 2} .0398351{col 42}{space 1}   -0.74{col 51}{space 3}0.459{col 59}{space 4}-.1077132{col 72}{space 3} .0487317
{txt}{space 2}Knowledge_Japan {c |}{col 19}{res}{space 2} .0555854{col 31}{space 2} .0353318{col 42}{space 1}    1.57{col 51}{space 3}0.116{col 59}{space 4} -.013794{col 72}{space 3} .1249649
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .3290471{col 31}{space 2} .1153995{col 42}{space 1}    2.85{col 51}{space 3}0.004{col 59}{space 4} .1024424{col 72}{space 3} .5556518
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model6
{txt}
{com}. 
. reg credibility_dummy i.condition if inattentive==0 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       677
{txt}{hline 13}{c +}{hline 34}   F(1, 675)       = {res}     1.42
{txt}       Model {c |} {res} .208960989         1  .208960989   {txt}Prob > F        ={res}    0.2334
{txt}    Residual {c |} {res} 99.1647465       675  .146910736   {txt}R-squared       ={res}    0.0021
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0006
{txt}       Total {c |} {res} 99.3737075       676  .147002526   {txt}Root MSE        =   {res} .38329

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}credibility_dummy{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.0351382{col 31}{space 2} .0294628{col 42}{space 1}   -1.19{col 51}{space 3}0.233{col 59}{space 4} -.092988{col 72}{space 3} .0227115
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .8387097{col 31}{space 2} .0207563{col 42}{space 1}   40.41{col 51}{space 3}0.000{col 59}{space 4}  .797955{col 72}{space 3} .8794643
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model7
{txt}
{com}. 
. reg credibility_dummy i.condition Male Age White Ideology Democrat Republican Income Voting_Pres20 Education Feeling_China Feeling_Japan Knowledge_China Knowledge_Japan if inattentive==0 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       640
{txt}{hline 13}{c +}{hline 34}   F(14, 625)      = {res}     4.08
{txt}       Model {c |} {res} 8.00793744        14  .571995532   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 87.6030001       625    .1401648   {txt}R-squared       ={res}    0.0838
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0632
{txt}       Total {c |} {res} 95.6109375       639   .14962588   {txt}Root MSE        =   {res} .37439

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}credibility_dummy{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.0348717{col 31}{space 2} .0299786{col 42}{space 1}   -1.16{col 51}{space 3}0.245{col 59}{space 4}-.0937427{col 72}{space 3} .0239994
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .0226131{col 31}{space 2} .0313758{col 42}{space 1}    0.72{col 51}{space 3}0.471{col 59}{space 4}-.0390016{col 72}{space 3} .0842279
{txt}{space 14}Age {c |}{col 19}{res}{space 2} .0047492{col 31}{space 2} .0122705{col 42}{space 1}    0.39{col 51}{space 3}0.699{col 59}{space 4}-.0193472{col 72}{space 3} .0288457
{txt}{space 12}White {c |}{col 19}{res}{space 2} .0415425{col 31}{space 2} .0336399{col 42}{space 1}    1.23{col 51}{space 3}0.217{col 59}{space 4}-.0245184{col 72}{space 3} .1076034
{txt}{space 9}Ideology {c |}{col 19}{res}{space 2}-.0074662{col 31}{space 2} .0142824{col 42}{space 1}   -0.52{col 51}{space 3}0.601{col 59}{space 4}-.0355135{col 72}{space 3} .0205811
{txt}{space 9}Democrat {c |}{col 19}{res}{space 2}-.0159039{col 31}{space 2} .0408648{col 42}{space 1}   -0.39{col 51}{space 3}0.697{col 59}{space 4}-.0961529{col 72}{space 3}  .064345
{txt}{space 7}Republican {c |}{col 19}{res}{space 2}-.0181859{col 31}{space 2} .0472775{col 42}{space 1}   -0.38{col 51}{space 3}0.701{col 59}{space 4}-.1110278{col 72}{space 3}  .074656
{txt}{space 11}Income {c |}{col 19}{res}{space 2}-.0043783{col 31}{space 2} .0073413{col 42}{space 1}   -0.60{col 51}{space 3}0.551{col 59}{space 4}-.0187949{col 72}{space 3} .0100382
{txt}{space 4}Voting_Pres20 {c |}{col 19}{res}{space 2} .0225411{col 31}{space 2} .0443707{col 42}{space 1}    0.51{col 51}{space 3}0.612{col 59}{space 4}-.0645926{col 72}{space 3} .1096748
{txt}{space 8}Education {c |}{col 19}{res}{space 2}-.0176803{col 31}{space 2} .0331594{col 42}{space 1}   -0.53{col 51}{space 3}0.594{col 59}{space 4}-.0827976{col 72}{space 3}  .047437
{txt}{space 4}Feeling_China {c |}{col 19}{res}{space 2}-.0272083{col 31}{space 2} .0107367{col 42}{space 1}   -2.53{col 51}{space 3}0.012{col 59}{space 4}-.0482927{col 72}{space 3}-.0061239
{txt}{space 4}Feeling_Japan {c |}{col 19}{res}{space 2} .0787965{col 31}{space 2} .0126654{col 42}{space 1}    6.22{col 51}{space 3}0.000{col 59}{space 4} .0539246{col 72}{space 3} .1036683
{txt}{space 2}Knowledge_China {c |}{col 19}{res}{space 2}-.0343166{col 31}{space 2} .0393771{col 42}{space 1}   -0.87{col 51}{space 3}0.384{col 59}{space 4} -.111644{col 72}{space 3} .0430108
{txt}{space 2}Knowledge_Japan {c |}{col 19}{res}{space 2} .0084438{col 31}{space 2} .0346705{col 42}{space 1}    0.24{col 51}{space 3}0.808{col 59}{space 4} -.059641{col 72}{space 3} .0765286
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .5145445{col 31}{space 2} .1227056{col 42}{space 1}    4.19{col 51}{space 3}0.000{col 59}{space 4} .2735794{col 72}{space 3} .7555096
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model8
{txt}
{com}. 
. esttab Model5 Model6 Model7 Model8 using inattentive_dummy.tex, replace se r2 legend label collabels(none) varlabels(_cons Constant) star(* 0.05 ** 0.01)
{res}{txt}(output written to {browse  `"inattentive_dummy.tex"'})

{com}. 
. **Tables 11-13: Manupulation Checks
. tab m1

         {txt}M1 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
 Don't know {c |}{res}        124        8.18        8.18
{txt}         No {c |}{res}        729       48.12       56.30
{txt}        Yes {c |}{res}        662       43.70      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,515      100.00
{txt}
{com}. tab m2

         {txt}M2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         No {c |}{res}      1,186       83.35       83.35
{txt}        Yes {c |}{res}        237       16.65      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,423      100.00
{txt}
{com}. gen manipulation=0
{txt}
{com}. replace manipulation=1 if condition==1 & m1=="No" & m2=="No"
{txt}(185 real changes made)

{com}. replace manipulation=1 if condition==2 & m1=="No" & m2=="Yes"
{txt}(65 real changes made)

{com}. replace manipulation=1 if condition==3 & m1=="No" & m2=="No"
{txt}(222 real changes made)

{com}. replace manipulation=1 if condition==4 & m1=="Yes" & m2=="No"
{txt}(211 real changes made)

{com}. tab manipulation

{txt}manipulatio {c |}
          n {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        832       54.92       54.92
{txt}          1 {c |}{res}        683       45.08      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,515      100.00
{txt}
{com}. tab condition manipulation, row chi2
{txt}
{c TLC}{hline 16}{c TRC}
{c |} Key{col 18}{c |}
{c LT}{hline 16}{c RT}
{c |}{space 3}{it:frequency}{col 18}{c |}
{c |}{space 1}{it:row percentage}{col 18}{c |}
{c BLC}{hline 16}{c BRC}

                    {c |}     manipulation
          condition {c |}         0          1 {c |}     Total
{hline 20}{c +}{hline 22}{c +}{hline 10}
China/No Commitment {c |}{res}       194        185 {txt}{c |}{res}       379 
                    {txt}{c |}{res}     51.19      48.81 {txt}{c |}{res}    100.00 
{txt}{hline 20}{c +}{hline 22}{c +}{hline 10}
   China/Commitment {c |}{res}       310         65 {txt}{c |}{res}       375 
                    {txt}{c |}{res}     82.67      17.33 {txt}{c |}{res}    100.00 
{txt}{hline 20}{c +}{hline 22}{c +}{hline 10}
Japan/No Commitmnet {c |}{res}       160        222 {txt}{c |}{res}       382 
                    {txt}{c |}{res}     41.88      58.12 {txt}{c |}{res}    100.00 
{txt}{hline 20}{c +}{hline 22}{c +}{hline 10}
   Japan/Commitment {c |}{res}       168        211 {txt}{c |}{res}       379 
                    {txt}{c |}{res}     44.33      55.67 {txt}{c |}{res}    100.00 
{txt}{hline 20}{c +}{hline 22}{c +}{hline 10}
              Total {c |}{res}       832        683 {txt}{c |}{res}     1,515 
                    {txt}{c |}{res}     54.92      45.08 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}3{txt}) = {res}162.1360  {txt} Pr = {res}0.000
{txt}
{com}. 
. *Continous
. reg credibility i.condition if manipulation==1 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       247
{txt}{hline 13}{c +}{hline 34}   F(1, 245)       = {res}    13.77
{txt}       Model {c |} {res} 8.60680176         1  8.60680176   {txt}Prob > F        ={res}    0.0003
{txt}    Residual {c |} {res} 153.134089       245  .625037098   {txt}R-squared       ={res}    0.0532
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0493
{txt}       Total {c |} {res} 161.740891       246  .657483295   {txt}Root MSE        =   {res} .79059

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.4305144{col 31}{space 2} .1160165{col 42}{space 1}   -3.71{col 51}{space 3}0.000{col 59}{space 4}-.6590313{col 72}{space 3}-.2019974
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.075676{col 31}{space 2} .0581255{col 42}{space 1}   35.71{col 51}{space 3}0.000{col 59}{space 4} 1.961186{col 72}{space 3} 2.190165
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model1
{txt}
{com}. 
. reg credibility i.condition Male Age White Ideology Democrat Republican Income Voting_Pres20 Education Feeling_China Feeling_Japan Knowledge_China Knowledge_Japan if manipulation==1 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       236
{txt}{hline 13}{c +}{hline 34}   F(14, 221)      = {res}     5.58
{txt}       Model {c |} {res} 39.6005439        14  2.82861028   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 111.975727       221  .506677499   {txt}R-squared       ={res}    0.2613
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2145
{txt}       Total {c |} {res} 151.576271       235  .645005409   {txt}Root MSE        =   {res} .71181

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.3377819{col 31}{space 2} .1096186{col 42}{space 1}   -3.08{col 51}{space 3}0.002{col 59}{space 4}-.5538135{col 72}{space 3}-.1217504
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .1371201{col 31}{space 2} .1041982{col 42}{space 1}    1.32{col 51}{space 3}0.190{col 59}{space 4}-.0682292{col 72}{space 3} .3424694
{txt}{space 14}Age {c |}{col 19}{res}{space 2}-.1173369{col 31}{space 2} .0397404{col 42}{space 1}   -2.95{col 51}{space 3}0.003{col 59}{space 4}-.1956556{col 72}{space 3}-.0390182
{txt}{space 12}White {c |}{col 19}{res}{space 2} .0530073{col 31}{space 2}  .117029{col 42}{space 1}    0.45{col 51}{space 3}0.651{col 59}{space 4}-.1776283{col 72}{space 3} .2836428
{txt}{space 9}Ideology {c |}{col 19}{res}{space 2} .0966805{col 31}{space 2} .0448794{col 42}{space 1}    2.15{col 51}{space 3}0.032{col 59}{space 4} .0082342{col 72}{space 3} .1851268
{txt}{space 9}Democrat {c |}{col 19}{res}{space 2}-.0970671{col 31}{space 2} .1351313{col 42}{space 1}   -0.72{col 51}{space 3}0.473{col 59}{space 4} -.363378{col 72}{space 3} .1692438
{txt}{space 7}Republican {c |}{col 19}{res}{space 2} .2859089{col 31}{space 2} .1523726{col 42}{space 1}    1.88{col 51}{space 3}0.062{col 59}{space 4}-.0143804{col 72}{space 3} .5861981
{txt}{space 11}Income {c |}{col 19}{res}{space 2}-.0014572{col 31}{space 2} .0240184{col 42}{space 1}   -0.06{col 51}{space 3}0.952{col 59}{space 4}-.0487916{col 72}{space 3} .0458772
{txt}{space 4}Voting_Pres20 {c |}{col 19}{res}{space 2} .1174604{col 31}{space 2} .1521377{col 42}{space 1}    0.77{col 51}{space 3}0.441{col 59}{space 4}-.1823659{col 72}{space 3} .4172866
{txt}{space 8}Education {c |}{col 19}{res}{space 2} .1340682{col 31}{space 2} .1059714{col 42}{space 1}    1.27{col 51}{space 3}0.207{col 59}{space 4}-.0747755{col 72}{space 3}  .342912
{txt}{space 4}Feeling_China {c |}{col 19}{res}{space 2} .1828154{col 31}{space 2} .0359084{col 42}{space 1}    5.09{col 51}{space 3}0.000{col 59}{space 4} .1120488{col 72}{space 3}  .253582
{txt}{space 4}Feeling_Japan {c |}{col 19}{res}{space 2} -.054098{col 31}{space 2} .0367164{col 42}{space 1}   -1.47{col 51}{space 3}0.142{col 59}{space 4}-.1264571{col 72}{space 3} .0182611
{txt}{space 2}Knowledge_China {c |}{col 19}{res}{space 2}-.0502014{col 31}{space 2} .1285263{col 42}{space 1}   -0.39{col 51}{space 3}0.696{col 59}{space 4}-.3034954{col 72}{space 3} .2030925
{txt}{space 2}Knowledge_Japan {c |}{col 19}{res}{space 2} -.055639{col 31}{space 2} .1081901{col 42}{space 1}   -0.51{col 51}{space 3}0.608{col 59}{space 4}-.2688554{col 72}{space 3} .1575774
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}  1.41615{col 31}{space 2} .3777493{col 42}{space 1}    3.75{col 51}{space 3}0.000{col 59}{space 4} .6716979{col 72}{space 3} 2.160601
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model2
{txt}
{com}. 
. reg credibility i.condition if manipulation==1 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       433
{txt}{hline 13}{c +}{hline 34}   F(1, 431)       = {res}    13.17
{txt}       Model {c |} {res} 5.33573032         1  5.33573032   {txt}Prob > F        ={res}    0.0003
{txt}    Residual {c |} {res} 174.655032       431  .405232092   {txt}R-squared       ={res}    0.0296
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0274
{txt}       Total {c |} {res} 179.990762       432  .416645283   {txt}Root MSE        =   {res} .63658

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2} -.222087{col 31}{space 2} .0612038{col 42}{space 1}   -3.63{col 51}{space 3}0.000{col 59}{space 4} -.342382{col 72}{space 3} -.101792
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.103604{col 31}{space 2} .0427243{col 42}{space 1}   72.64{col 51}{space 3}0.000{col 59}{space 4}  3.01963{col 72}{space 3} 3.187578
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model3
{txt}
{com}. 
. reg credibility i.condition Male Age White Ideology Democrat Republican Income Voting_Pres20 Education Feeling_China Feeling_Japan Knowledge_China Knowledge_Japan if manipulation==1 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       410
{txt}{hline 13}{c +}{hline 34}   F(14, 395)      = {res}     6.10
{txt}       Model {c |} {res} 29.8561151        14  2.13257965   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 138.134129       395  .349706655   {txt}R-squared       ={res}    0.1777
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1486
{txt}       Total {c |} {res} 167.990244       409  .410734093   {txt}Root MSE        =   {res} .59136

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.1776612{col 31}{space 2} .0598482{col 42}{space 1}   -2.97{col 51}{space 3}0.003{col 59}{space 4} -.295322{col 72}{space 3}-.0600004
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .0706457{col 31}{space 2} .0636333{col 42}{space 1}    1.11{col 51}{space 3}0.268{col 59}{space 4}-.0544566{col 72}{space 3}  .195748
{txt}{space 14}Age {c |}{col 19}{res}{space 2} .0005888{col 31}{space 2} .0239209{col 42}{space 1}    0.02{col 51}{space 3}0.980{col 59}{space 4}-.0464394{col 72}{space 3}  .047617
{txt}{space 12}White {c |}{col 19}{res}{space 2}  .081483{col 31}{space 2} .0671558{col 42}{space 1}    1.21{col 51}{space 3}0.226{col 59}{space 4}-.0505445{col 72}{space 3} .2135104
{txt}{space 9}Ideology {c |}{col 19}{res}{space 2}-.0153406{col 31}{space 2} .0274368{col 42}{space 1}   -0.56{col 51}{space 3}0.576{col 59}{space 4}-.0692809{col 72}{space 3} .0385998
{txt}{space 9}Democrat {c |}{col 19}{res}{space 2} -.033011{col 31}{space 2} .0805904{col 42}{space 1}   -0.41{col 51}{space 3}0.682{col 59}{space 4}-.1914507{col 72}{space 3} .1254287
{txt}{space 7}Republican {c |}{col 19}{res}{space 2}-.1085786{col 31}{space 2} .0928909{col 42}{space 1}   -1.17{col 51}{space 3}0.243{col 59}{space 4}-.2912009{col 72}{space 3} .0740438
{txt}{space 11}Income {c |}{col 19}{res}{space 2} .0034001{col 31}{space 2} .0144019{col 42}{space 1}    0.24{col 51}{space 3}0.813{col 59}{space 4}-.0249139{col 72}{space 3}  .031714
{txt}{space 4}Voting_Pres20 {c |}{col 19}{res}{space 2}-.0111279{col 31}{space 2} .0902803{col 42}{space 1}   -0.12{col 51}{space 3}0.902{col 59}{space 4}-.1886178{col 72}{space 3}  .166362
{txt}{space 8}Education {c |}{col 19}{res}{space 2}-.1972949{col 31}{space 2} .0671475{col 42}{space 1}   -2.94{col 51}{space 3}0.003{col 59}{space 4}-.3293061{col 72}{space 3}-.0652836
{txt}{space 4}Feeling_China {c |}{col 19}{res}{space 2}-.0501889{col 31}{space 2} .0239839{col 42}{space 1}   -2.09{col 51}{space 3}0.037{col 59}{space 4} -.097341{col 72}{space 3}-.0030369
{txt}{space 4}Feeling_Japan {c |}{col 19}{res}{space 2} .1564256{col 31}{space 2} .0257039{col 42}{space 1}    6.09{col 51}{space 3}0.000{col 59}{space 4}  .105892{col 72}{space 3} .2069591
{txt}{space 2}Knowledge_China {c |}{col 19}{res}{space 2} .0723015{col 31}{space 2} .0830987{col 42}{space 1}    0.87{col 51}{space 3}0.385{col 59}{space 4}-.0910695{col 72}{space 3} .2356724
{txt}{space 2}Knowledge_Japan {c |}{col 19}{res}{space 2} .0653447{col 31}{space 2} .0680197{col 42}{space 1}    0.96{col 51}{space 3}0.337{col 59}{space 4}-.0683811{col 72}{space 3} .1990705
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.442204{col 31}{space 2} .2513946{col 42}{space 1}    9.71{col 51}{space 3}0.000{col 59}{space 4} 1.947965{col 72}{space 3} 2.936442
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model4
{txt}
{com}. 
. esttab Model1 Model2 Model3 Model4 using manipulation_continous.tex, replace se r2 legend label collabels(none) varlabels(_cons Constant) star(* 0.05 ** 0.01)
{res}{txt}(output written to {browse  `"manipulation_continous.tex"'})

{com}. 
. *Dichotomous
. reg credibility_dummy i.condition if manipulation==1 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       247
{txt}{hline 13}{c +}{hline 34}   F(1, 245)       = {res}    10.89
{txt}       Model {c |} {res} 1.86559587         1  1.86559587   {txt}Prob > F        ={res}    0.0011
{txt}    Residual {c |} {res}  41.980558       245  .171349216   {txt}R-squared       ={res}    0.0425
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0386
{txt}       Total {c |} {res} 43.8461538       246  .178236398   {txt}Root MSE        =   {res} .41394

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}credibility_dummy{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.2004359{col 31}{space 2} .0607446{col 42}{space 1}   -3.30{col 51}{space 3}0.001{col 59}{space 4}-.3200842{col 72}{space 3}-.0807876
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .2810811{col 31}{space 2} .0304337{col 42}{space 1}    9.24{col 51}{space 3}0.000{col 59}{space 4} .2211359{col 72}{space 3} .3410262
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model5
{txt}
{com}. 
. reg credibility_dummy i.condition Male Age White Ideology Democrat Republican Income Voting_Pres20 Education Feeling_China Feeling_Japan Knowledge_China Knowledge_Japan if manipulation==1 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       236
{txt}{hline 13}{c +}{hline 34}   F(14, 221)      = {res}     2.62
{txt}       Model {c |} {res} 5.84815389        14  .417725278   {txt}Prob > F        ={res}    0.0016
{txt}    Residual {c |} {res} 35.2493037       221  .159499112   {txt}R-squared       ={res}    0.1423
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0880
{txt}       Total {c |} {res} 41.0974576       235  .174882798   {txt}Root MSE        =   {res} .39937

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}credibility_dummy{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.1702736{col 31}{space 2} .0615032{col 42}{space 1}   -2.77{col 51}{space 3}0.006{col 59}{space 4}-.2914813{col 72}{space 3}-.0490658
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .0926196{col 31}{space 2}  .058462{col 42}{space 1}    1.58{col 51}{space 3}0.115{col 59}{space 4}-.0225948{col 72}{space 3} .2078339
{txt}{space 14}Age {c |}{col 19}{res}{space 2}-.0551999{col 31}{space 2}  .022297{col 42}{space 1}   -2.48{col 51}{space 3}0.014{col 59}{space 4}-.0991418{col 72}{space 3} -.011258
{txt}{space 12}White {c |}{col 19}{res}{space 2}  .085882{col 31}{space 2} .0656609{col 42}{space 1}    1.31{col 51}{space 3}0.192{col 59}{space 4}-.0435196{col 72}{space 3} .2152836
{txt}{space 9}Ideology {c |}{col 19}{res}{space 2} .0200918{col 31}{space 2} .0251802{col 42}{space 1}    0.80{col 51}{space 3}0.426{col 59}{space 4}-.0295323{col 72}{space 3} .0697159
{txt}{space 9}Democrat {c |}{col 19}{res}{space 2}-.0099692{col 31}{space 2} .0758175{col 42}{space 1}   -0.13{col 51}{space 3}0.896{col 59}{space 4} -.159387{col 72}{space 3} .1394486
{txt}{space 7}Republican {c |}{col 19}{res}{space 2} .1127334{col 31}{space 2}  .085491{col 42}{space 1}    1.32{col 51}{space 3}0.189{col 59}{space 4}-.0557485{col 72}{space 3} .2812152
{txt}{space 11}Income {c |}{col 19}{res}{space 2}-.0024804{col 31}{space 2} .0134759{col 42}{space 1}   -0.18{col 51}{space 3}0.854{col 59}{space 4}-.0290381{col 72}{space 3} .0240773
{txt}{space 4}Voting_Pres20 {c |}{col 19}{res}{space 2} .0881683{col 31}{space 2} .0853591{col 42}{space 1}    1.03{col 51}{space 3}0.303{col 59}{space 4}-.0800538{col 72}{space 3} .2563903
{txt}{space 8}Education {c |}{col 19}{res}{space 2} .0468018{col 31}{space 2} .0594568{col 42}{space 1}    0.79{col 51}{space 3}0.432{col 59}{space 4}-.0703732{col 72}{space 3} .1639767
{txt}{space 4}Feeling_China {c |}{col 19}{res}{space 2} .0531537{col 31}{space 2} .0201469{col 42}{space 1}    2.64{col 51}{space 3}0.009{col 59}{space 4}  .013449{col 72}{space 3} .0928584
{txt}{space 4}Feeling_Japan {c |}{col 19}{res}{space 2}-.0077262{col 31}{space 2} .0206003{col 42}{space 1}   -0.38{col 51}{space 3}0.708{col 59}{space 4}-.0483244{col 72}{space 3}  .032872
{txt}{space 2}Knowledge_China {c |}{col 19}{res}{space 2}-.0498578{col 31}{space 2} .0721116{col 42}{space 1}   -0.69{col 51}{space 3}0.490{col 59}{space 4}-.1919722{col 72}{space 3} .0922566
{txt}{space 2}Knowledge_Japan {c |}{col 19}{res}{space 2}-.0059777{col 31}{space 2} .0607017{col 42}{space 1}   -0.10{col 51}{space 3}0.922{col 59}{space 4} -.125606{col 72}{space 3} .1136506
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .0263001{col 31}{space 2}  .211942{col 42}{space 1}    0.12{col 51}{space 3}0.901{col 59}{space 4}-.3913859{col 72}{space 3}  .443986
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model6
{txt}
{com}. 
. reg credibility_dummy i.condition if manipulation==1 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       433
{txt}{hline 13}{c +}{hline 34}   F(1, 431)       = {res}     6.91
{txt}       Model {c |} {res} 1.01980605         1  1.01980605   {txt}Prob > F        ={res}    0.0089
{txt}    Residual {c |} {res}  63.566799       431  .147486773   {txt}R-squared       ={res}    0.0158
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0135
{txt}       Total {c |} {res} 64.5866051       432   .14950603   {txt}Root MSE        =   {res} .38404

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}credibility_dummy{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.0970924{col 31}{space 2} .0369235{col 42}{space 1}   -2.63{col 51}{space 3}0.009{col 59}{space 4}-.1696649{col 72}{space 3}-.0245198
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .8648649{col 31}{space 2} .0257751{col 42}{space 1}   33.55{col 51}{space 3}0.000{col 59}{space 4} .8142044{col 72}{space 3} .9155254
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model7
{txt}
{com}. 
. reg credibility_dummy i.condition Male Age White Ideology Democrat Republican Income Voting_Pres20 Education Feeling_China Feeling_Japan Knowledge_China Knowledge_Japan if manipulation==1 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       410
{txt}{hline 13}{c +}{hline 34}   F(14, 395)      = {res}     2.86
{txt}       Model {c |} {res} 5.52448937        14  .394606384   {txt}Prob > F        ={res}    0.0004
{txt}    Residual {c |} {res} 54.4779497       395   .13791886   {txt}R-squared       ={res}    0.0921
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0599
{txt}       Total {c |} {res}  60.002439       409   .14670523   {txt}Root MSE        =   {res} .37137

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}credibility_dummy{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.0693181{col 31}{space 2} .0375847{col 42}{space 1}   -1.84{col 51}{space 3}0.066{col 59}{space 4}-.1432091{col 72}{space 3} .0045729
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .0238647{col 31}{space 2} .0399617{col 42}{space 1}    0.60{col 51}{space 3}0.551{col 59}{space 4}-.0546996{col 72}{space 3} .1024289
{txt}{space 14}Age {c |}{col 19}{res}{space 2}  .004264{col 31}{space 2} .0150223{col 42}{space 1}    0.28{col 51}{space 3}0.777{col 59}{space 4}-.0252697{col 72}{space 3} .0337978
{txt}{space 12}White {c |}{col 19}{res}{space 2} .0666441{col 31}{space 2} .0421739{col 42}{space 1}    1.58{col 51}{space 3}0.115{col 59}{space 4}-.0162692{col 72}{space 3} .1495574
{txt}{space 9}Ideology {c |}{col 19}{res}{space 2}-.0045991{col 31}{space 2} .0172303{col 42}{space 1}   -0.27{col 51}{space 3}0.790{col 59}{space 4}-.0384736{col 72}{space 3} .0292754
{txt}{space 9}Democrat {c |}{col 19}{res}{space 2}-.0151343{col 31}{space 2} .0506108{col 42}{space 1}   -0.30{col 51}{space 3}0.765{col 59}{space 4}-.1146345{col 72}{space 3} .0843659
{txt}{space 7}Republican {c |}{col 19}{res}{space 2}-.0609833{col 31}{space 2} .0583355{col 42}{space 1}   -1.05{col 51}{space 3}0.296{col 59}{space 4}-.1756702{col 72}{space 3} .0537036
{txt}{space 11}Income {c |}{col 19}{res}{space 2}-.0032291{col 31}{space 2} .0090444{col 42}{space 1}   -0.36{col 51}{space 3}0.721{col 59}{space 4}-.0210103{col 72}{space 3} .0145521
{txt}{space 4}Voting_Pres20 {c |}{col 19}{res}{space 2}-.0121076{col 31}{space 2}  .056696{col 42}{space 1}   -0.21{col 51}{space 3}0.831{col 59}{space 4}-.1235713{col 72}{space 3} .0993561
{txt}{space 8}Education {c |}{col 19}{res}{space 2}-.0515665{col 31}{space 2} .0421687{col 42}{space 1}   -1.22{col 51}{space 3}0.222{col 59}{space 4}-.1344696{col 72}{space 3} .0313366
{txt}{space 4}Feeling_China {c |}{col 19}{res}{space 2}-.0167374{col 31}{space 2} .0150619{col 42}{space 1}   -1.11{col 51}{space 3}0.267{col 59}{space 4}-.0463489{col 72}{space 3} .0128741
{txt}{space 4}Feeling_Japan {c |}{col 19}{res}{space 2} .0733959{col 31}{space 2} .0161421{col 42}{space 1}    4.55{col 51}{space 3}0.000{col 59}{space 4} .0416608{col 72}{space 3}  .105131
{txt}{space 2}Knowledge_China {c |}{col 19}{res}{space 2}-.0025092{col 31}{space 2}  .052186{col 42}{space 1}   -0.05{col 51}{space 3}0.962{col 59}{space 4}-.1051062{col 72}{space 3} .1000878
{txt}{space 2}Knowledge_Japan {c |}{col 19}{res}{space 2} .0352612{col 31}{space 2} .0427164{col 42}{space 1}    0.83{col 51}{space 3}0.410{col 59}{space 4}-.0487187{col 72}{space 3} .1192411
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}  .520684{col 31}{space 2} .1578759{col 42}{space 1}    3.30{col 51}{space 3}0.001{col 59}{space 4} .2103019{col 72}{space 3} .8310661
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Model8
{txt}
{com}. 
. esttab Model5 Model6 Model7 Model8 using manipulation_dummy.tex, replace se r2 legend label collabels(none) varlabels(_cons Constant) star(* 0.05 ** 0.01)
{res}{txt}(output written to {browse  `"manipulation_dummy.tex"'})

{com}. 
. ***Figues A1-A22: The Heterogenous Effect of Demographic and Attitudinal Variables
. **Heterogenous Effect (China)
. *Conditional Effect of Gender?
. regress credibility i.condition##i.Male if condition==1 | condition==2

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       736
{txt}{hline 13}{c +}{hline 34}   F(3, 732)       = {res}    17.42
{txt}       Model {c |} {res} 31.8505318         3  10.6168439   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}  446.23099       732  .609605178   {txt}R-squared       ={res}    0.0666
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0628
{txt}       Total {c |} {res} 478.081522       735   .65045105   {txt}Root MSE        =   {res} .78077

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        credibility{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}condition {c |}
{space 2}China/Commitment  {c |}{col 21}{res}{space 2}-.2825468{col 33}{space 2} .0826127{col 44}{space 1}   -3.42{col 53}{space 3}0.001{col 61}{space 4} -.444733{col 74}{space 3}-.1203607
{txt}{space 13}1.Male {c |}{col 21}{res}{space 2} .1283422{col 33}{space 2} .0807455{col 44}{space 1}    1.59{col 53}{space 3}0.112{col 61}{space 4}-.0301782{col 74}{space 3} .2868627
{txt}{space 19} {c |}
{space 5}condition#Male {c |}
China/Commitment#1  {c |}{col 21}{res}{space 2}-.2288903{col 33}{space 2} .1152234{col 44}{space 1}   -1.99{col 53}{space 3}0.047{col 61}{space 4}-.4550981{col 74}{space 3}-.0026825
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 2.042781{col 33}{space 2} .0570957{col 44}{space 1}   35.78{col 53}{space 3}0.000{col 61}{space 4}  1.93069{col 74}{space 3} 2.154872
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Male==0 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       358
{txt}{hline 13}{c +}{hline 34}   F(1, 356)       = {res}    13.16
{txt}       Model {c |} {res}  7.1307559         1   7.1307559   {txt}Prob > F        ={res}    0.0003
{txt}    Residual {c |} {res} 192.827345       356  .541649845   {txt}R-squared       ={res}    0.0357
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0330
{txt}       Total {c |} {res} 199.958101       357  .560106724   {txt}Root MSE        =   {res} .73597

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.2825468{col 31}{space 2} .0778721{col 42}{space 1}   -3.63{col 51}{space 3}0.000{col 59}{space 4} -.435694{col 72}{space 3}-.1293996
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.042781{col 31}{space 2} .0538194{col 42}{space 1}   37.96{col 51}{space 3}0.000{col 59}{space 4} 1.936937{col 72}{space 3} 2.148625
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotMale
{txt}
{com}. 
. regress credibility i.condition if Male==1 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       378
{txt}{hline 13}{c +}{hline 34}   F(1, 376)       = {res}    36.67
{txt}       Model {c |} {res} 24.7154023         1  24.7154023   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 253.403645       376  .673945865   {txt}R-squared       ={res}    0.0889
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0864
{txt}       Total {c |} {res} 278.119048       377  .737716307   {txt}Root MSE        =   {res} .82094

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.5114371{col 31}{space 2} .0844541{col 42}{space 1}   -6.06{col 51}{space 3}0.000{col 59}{space 4}-.6774987{col 72}{space 3}-.3453755
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.171123{col 31}{space 2} .0600332{col 42}{space 1}   36.17{col 51}{space 3}0.000{col 59}{space 4}  2.05308{col 72}{space 3} 2.289166
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Male
{txt}
{com}. 
. coefplot NotMale Male, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Male), size(medium))
{res}{txt}
{com}. graph export "china_male.pdf", replace
{txt}(file china_male.pdf written in PDF format)

{com}. 
. *Conditional Effect of Age?
. sum Age

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}Age {c |}{res}      1,515    3.183498    1.294993          1          6
{txt}
{com}. gen Old=.
{txt}(1,515 missing values generated)

{com}. replace Old=1 if Age>4
{txt}(254 real changes made)

{com}. replace Old=0 if Age<=4
{txt}(1,261 real changes made)

{com}. tab Old

        {txt}Old {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,261       83.23       83.23
{txt}          1 {c |}{res}        254       16.77      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,515      100.00
{txt}
{com}. 
. regress credibility i.condition##i.Old if condition==1 | condition==2

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       746
{txt}{hline 13}{c +}{hline 34}   F(3, 742)       = {res}    23.25
{txt}       Model {c |} {res}  41.302995         3   13.767665   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 439.314967       742  .592068689   {txt}R-squared       ={res}    0.0859
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0822
{txt}       Total {c |} {res} 480.617962       745  .645124782   {txt}Root MSE        =   {res} .76946

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        credibility{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}condition {c |}
{space 2}China/Commitment  {c |}{col 21}{res}{space 2}-.4331446{col 33}{space 2}  .062013{col 44}{space 1}   -6.98{col 53}{space 3}0.000{col 61}{space 4}-.5548865{col 74}{space 3}-.3114027
{txt}{space 14}1.Old {c |}{col 21}{res}{space 2}-.4181431{col 33}{space 2} .1042225{col 44}{space 1}   -4.01{col 53}{space 3}0.000{col 61}{space 4}-.6227492{col 74}{space 3} -.213537
{txt}{space 19} {c |}
{space 6}condition#Old {c |}
China/Commitment#1  {c |}{col 21}{res}{space 2} .2068188{col 33}{space 2} .1485511{col 44}{space 1}    1.39{col 53}{space 3}0.164{col 61}{space 4}-.0848117{col 74}{space 3} .4984493
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 2.175719{col 33}{space 2} .0434925{col 44}{space 1}   50.03{col 53}{space 3}0.000{col 61}{space 4} 2.090336{col 74}{space 3} 2.261102
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Old==0 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       616
{txt}{hline 13}{c +}{hline 34}   F(1, 614)       = {res}    47.77
{txt}       Model {c |} {res} 28.8849784         1  28.8849784   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 371.256255       614  .604651882   {txt}R-squared       ={res}    0.0722
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0707
{txt}       Total {c |} {res} 400.141234       615  .650636152   {txt}Root MSE        =   {res} .77759

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.4331446{col 31}{space 2} .0626685{col 42}{space 1}   -6.91{col 51}{space 3}0.000{col 59}{space 4}-.5562153{col 72}{space 3}-.3100739
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.175719{col 31}{space 2} .0439522{col 42}{space 1}   49.50{col 51}{space 3}0.000{col 59}{space 4} 2.089404{col 72}{space 3} 2.262034
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotOld
{txt}
{com}. 
. regress credibility i.condition if Old==1 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       130
{txt}{hline 13}{c +}{hline 34}   F(1, 128)       = {res}     3.13
{txt}       Model {c |} {res}  1.6643648         1   1.6643648   {txt}Prob > F        ={res}    0.0792
{txt}    Residual {c |} {res} 68.0587121       128  .531708688   {txt}R-squared       ={res}    0.0239
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0162
{txt}       Total {c |} {res} 69.7230769       129  .540488968   {txt}Root MSE        =   {res} .72918

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.2263258{col 31}{space 2} .1279224{col 42}{space 1}   -1.77{col 51}{space 3}0.079{col 59}{space 4}-.4794421{col 72}{space 3} .0267906
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.757576{col 31}{space 2} .0897563{col 42}{space 1}   19.58{col 51}{space 3}0.000{col 59}{space 4} 1.579978{col 72}{space 3} 1.935174
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Old
{txt}
{com}. 
. coefplot NotOld Old, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Old), size(medium))
{res}{txt}
{com}. graph export "china_old.pdf", replace
{txt}(file china_old.pdf written in PDF format)

{com}. 
. *Conditional Effect of White?
. regress credibility i.condition##i.White if condition==1 | condition==2

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       742
{txt}{hline 13}{c +}{hline 34}   F(3, 738)       = {res}    21.13
{txt}       Model {c |} {res} 37.7659578         3  12.5886526   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 439.630269       738  .595704971   {txt}R-squared       ={res}    0.0791
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0754
{txt}       Total {c |} {res} 477.396226       741  .644259415   {txt}Root MSE        =   {res} .77182

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            credibility{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      t{col 57}   P>|t|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}condition {c |}
{space 6}China/Commitment  {c |}{col 25}{res}{space 2}-.4374795{col 37}{space 2} .1038629{col 48}{space 1}   -4.21{col 57}{space 3}0.000{col 65}{space 4}-.6413816{col 78}{space 3}-.2335775
{txt}{space 23} {c |}
{space 18}White {c |}
{space 17}White  {c |}{col 25}{res}{space 2}-.2609176{col 37}{space 2} .0879691{col 48}{space 1}   -2.97{col 57}{space 3}0.003{col 65}{space 4}-.4336172{col 78}{space 3}-.0882181
{txt}{space 23} {c |}
{space 8}condition#White {c |}
China/Commitment#White  {c |}{col 25}{res}{space 2} .0516763{col 37}{space 2} .1239551{col 48}{space 1}    0.42{col 57}{space 3}0.677{col 65}{space 4}-.1916702{col 78}{space 3} .2950229
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} 2.287037{col 37}{space 2} .0742683{col 48}{space 1}   30.79{col 57}{space 3}0.000{col 65}{space 4} 2.141235{col 78}{space 3} 2.432839
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if White==0 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       221
{txt}{hline 13}{c +}{hline 34}   F(1, 219)       = {res}    16.24
{txt}       Model {c |} {res} 10.5687924         1  10.5687924   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res}  142.54433       219   .65088735   {txt}R-squared       ={res}    0.0690
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0648
{txt}       Total {c |} {res} 153.113122       220  .695968737   {txt}Root MSE        =   {res} .80678

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.4374795{col 31}{space 2}  .108567{col 42}{space 1}   -4.03{col 51}{space 3}0.000{col 59}{space 4}-.6514494{col 72}{space 3}-.2235096
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.287037{col 31}{space 2}  .077632{col 42}{space 1}   29.46{col 51}{space 3}0.000{col 59}{space 4} 2.134036{col 72}{space 3} 2.440039
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotWhite
{txt}
{com}. 
. regress credibility i.condition if White==1 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       521
{txt}{hline 13}{c +}{hline 34}   F(1, 519)       = {res}    33.84
{txt}       Model {c |} {res} 19.3708749         1  19.3708749   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 297.085939       519  .572419921   {txt}R-squared       ={res}    0.0612
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0594
{txt}       Total {c |} {res} 316.456814       520  .608570796   {txt}Root MSE        =   {res} .75658

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.3858032{col 31}{space 2} .0663206{col 42}{space 1}   -5.82{col 51}{space 3}0.000{col 59}{space 4}-.5160931{col 72}{space 3}-.2555133
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.026119{col 31}{space 2} .0462157{col 42}{space 1}   43.84{col 51}{space 3}0.000{col 59}{space 4} 1.935326{col 72}{space 3} 2.116912
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store White
{txt}
{com}. 
. coefplot NotWhite White, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (White), size(medium))
{res}{txt}
{com}. graph export "china_white.pdf", replace
{txt}(file china_white.pdf written in PDF format)

{com}. 
. *Conditional Effect of Ideology?
. sum Ideology

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}Ideology {c |}{res}      1,509    4.245858    1.817938          1          7
{txt}
{com}. gen Liberal=.
{txt}(1,515 missing values generated)

{com}. replace Liberal=1 if Ideology>5
{txt}(492 real changes made)

{com}. replace Liberal=0 if Ideology<=5
{txt}(1,023 real changes made)

{com}. tab Liberal

    {txt}Liberal {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,023       67.52       67.52
{txt}          1 {c |}{res}        492       32.48      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,515      100.00
{txt}
{com}. 
. regress credibility i.condition##i.Liberal if condition==1 | condition==2

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       746
{txt}{hline 13}{c +}{hline 34}   F(3, 742)       = {res}    16.63
{txt}       Model {c |} {res} 30.2844005         3  10.0948002   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 450.333562       742  .606918547   {txt}R-squared       ={res}    0.0630
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0592
{txt}       Total {c |} {res} 480.617962       745  .645124782   {txt}Root MSE        =   {res} .77905

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        credibility{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}condition {c |}
{space 2}China/Commitment  {c |}{col 21}{res}{space 2}-.4291339{col 33}{space 2} .0691295{col 44}{space 1}   -6.21{col 53}{space 3}0.000{col 61}{space 4}-.5648465{col 74}{space 3}-.2934212
{txt}{space 10}1.Liberal {c |}{col 21}{res}{space 2} .0016378{col 33}{space 2} .0851163{col 44}{space 1}    0.02{col 53}{space 3}0.985{col 61}{space 4}-.1654597{col 74}{space 3} .1687353
{txt}{space 19} {c |}
{space 2}condition#Liberal {c |}
China/Commitment#1  {c |}{col 21}{res}{space 2} .1038949{col 33}{space 2} .1224957{col 44}{space 1}    0.85{col 53}{space 3}0.397{col 61}{space 4}-.1365845{col 74}{space 3} .3443743
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 2.102362{col 33}{space 2} .0488819{col 44}{space 1}   43.01{col 53}{space 3}0.000{col 61}{space 4} 2.006399{col 74}{space 3} 2.198326
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Liberal==0 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       508
{txt}{hline 13}{c +}{hline 34}   F(1, 506)       = {res}    36.39
{txt}       Model {c |} {res} 23.3877953         1  23.3877953   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 325.216535       506  .642720426   {txt}R-squared       ={res}    0.0671
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0652
{txt}       Total {c |} {res} 348.604331       507  .687582506   {txt}Root MSE        =   {res}  .8017

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.4291339{col 31}{space 2} .0711392{col 42}{space 1}   -6.03{col 51}{space 3}0.000{col 59}{space 4}-.5688985{col 72}{space 3}-.2893692
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.102362{col 31}{space 2}  .050303{col 42}{space 1}   41.79{col 51}{space 3}0.000{col 59}{space 4} 2.003534{col 72}{space 3} 2.201191
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotLiberal
{txt}
{com}. 
. regress credibility i.condition if Liberal==1 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       238
{txt}{hline 13}{c +}{hline 34}   F(1, 236)       = {res}    11.84
{txt}       Model {c |} {res} 6.27793143         1  6.27793143   {txt}Prob > F        ={res}    0.0007
{txt}    Residual {c |} {res} 125.117027       236  .530156892   {txt}R-squared       ={res}    0.0478
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0437
{txt}       Total {c |} {res} 131.394958       237  .554409105   {txt}Root MSE        =   {res} .72812

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.3252389{col 31}{space 2} .0945141{col 42}{space 1}   -3.44{col 51}{space 3}0.001{col 59}{space 4} -.511438{col 72}{space 3}-.1390399
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}    2.104{col 31}{space 2} .0651249{col 42}{space 1}   32.31{col 51}{space 3}0.000{col 59}{space 4}   1.9757{col 72}{space 3}   2.2323
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Liberal
{txt}
{com}. 
. coefplot NotLiberal Liberal, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Ideology), size(medium))
{res}{txt}
{com}. graph export "china_liberal.pdf", replace
{txt}(file china_liberal.pdf written in PDF format)

{com}. 
. *Conditional Effect of Democrat?
. regress credibility i.condition##i.Democrat if condition==1 | condition==2

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       744
{txt}{hline 13}{c +}{hline 34}   F(3, 740)       = {res}    17.91
{txt}       Model {c |} {res} 32.4219793         3  10.8073264   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 446.544419       740  .603438403   {txt}R-squared       ={res}    0.0677
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0639
{txt}       Total {c |} {res} 478.966398       743   .64463849   {txt}Root MSE        =   {res} .77681

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}               credibility{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}condition {c |}
{space 9}China/Commitment  {c |}{col 28}{res}{space 2}-.4838352{col 40}{space 2} .0693222{col 51}{space 1}   -6.98{col 60}{space 3}0.000{col 68}{space 4}-.6199269{col 81}{space 3}-.3477436
{txt}{space 26} {c |}
{space 18}Democrat {c |}
{space 17}Democrat  {c |}{col 28}{res}{space 2}-.1532567{col 40}{space 2}  .086427{col 51}{space 1}   -1.77{col 60}{space 3}0.077{col 68}{space 4}-.3229279{col 81}{space 3} .0164145
{txt}{space 26} {c |}
{space 8}condition#Democrat {c |}
China/Commitment#Democrat  {c |}{col 28}{res}{space 2} .2660933{col 40}{space 2} .1217769{col 51}{space 1}    2.19{col 60}{space 3}0.029{col 68}{space 4}  .027024{col 81}{space 3} .5051626
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 2.153257{col 40}{space 2} .0480835{col 51}{space 1}   44.78{col 60}{space 3}0.000{col 68}{space 4}  2.05886{col 81}{space 3} 2.247653
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Democrat==0 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       503
{txt}{hline 13}{c +}{hline 34}   F(1, 501)       = {res}    46.69
{txt}       Model {c |} {res} 29.3956347         1  29.3956347   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 315.423451       501  .629587726   {txt}R-squared       ={res}    0.0852
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0834
{txt}       Total {c |} {res} 344.819085       502  .686890609   {txt}Root MSE        =   {res} .79347

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.4838352{col 31}{space 2} .0708083{col 42}{space 1}   -6.83{col 51}{space 3}0.000{col 59}{space 4} -.622953{col 72}{space 3}-.3447174
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.153257{col 31}{space 2} .0491143{col 42}{space 1}   43.84{col 51}{space 3}0.000{col 59}{space 4} 2.056761{col 72}{space 3} 2.249752
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotDemocrat
{txt}
{com}. 
. regress credibility i.condition if Democrat==1 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       241
{txt}{hline 13}{c +}{hline 34}   F(1, 239)       = {res}     5.20
{txt}       Model {c |} {res} 2.85413599         1  2.85413599   {txt}Prob > F        ={res}    0.0234
{txt}    Residual {c |} {res} 131.120968       239  .548623296   {txt}R-squared       ={res}    0.0213
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0172
{txt}       Total {c |} {res} 133.975104       240  .558229599   {txt}Root MSE        =   {res} .74069

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.2177419{col 31}{space 2} .0954645{col 42}{space 1}   -2.28{col 51}{space 3}0.023{col 59}{space 4}-.4058012{col 72}{space 3}-.0296827
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}        2{col 31}{space 2} .0684769{col 42}{space 1}   29.21{col 51}{space 3}0.000{col 59}{space 4} 1.865105{col 72}{space 3} 2.134895
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Democrat
{txt}
{com}. 
. coefplot NotDemocrat Democrat, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Democrat), size(medium))
{res}{txt}
{com}. graph export "china_democrat.pdf", replace
{txt}(file china_democrat.pdf written in PDF format)

{com}. 
. *Conditional Effect of Republican?
. regress credibility i.condition##i.Republican if condition==1 | condition==2

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       744
{txt}{hline 13}{c +}{hline 34}   F(3, 740)       = {res}    16.77
{txt}       Model {c |} {res} 30.4879182         3  10.1626394   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}  448.47848       740  .606051999   {txt}R-squared       ={res}    0.0637
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0599
{txt}       Total {c |} {res} 478.966398       743   .64463849   {txt}Root MSE        =   {res} .77849

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 credibility{col 30}{c |}      Coef.{col 42}   Std. Err.{col 54}      t{col 62}   P>|t|{col 70}     [95% Con{col 83}f. Interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}condition {c |}
{space 11}China/Commitment  {c |}{col 30}{res}{space 2} -.347821{col 42}{space 2}  .070273{col 53}{space 1}   -4.95{col 62}{space 3}0.000{col 70}{space 4}-.4857792{col 83}{space 3}-.2098629
{txt}{space 28} {c |}
{space 18}Republican {c |}
{space 17}Republican  {c |}{col 30}{res}{space 2}  .052929{col 42}{space 2} .0834303{col 53}{space 1}    0.63{col 62}{space 3}0.526{col 70}{space 4}-.1108592{col 83}{space 3} .2167173
{txt}{space 28} {c |}
{space 8}condition#Republican {c |}
China/Commitment#Republican  {c |}{col 30}{res}{space 2}-.1508592{col 42}{space 2} .1207248{col 53}{space 1}   -1.25{col 62}{space 3}0.212{col 70}{space 4}-.3878632{col 83}{space 3} .0861448
{txt}{space 28} {c |}
{space 23}_cons {c |}{col 30}{res}{space 2} 2.086777{col 42}{space 2} .0500435{col 53}{space 1}   41.70{col 62}{space 3}0.000{col 70}{space 4} 1.988533{col 83}{space 3} 2.185021
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Republican==0 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       491
{txt}{hline 13}{c +}{hline 34}   F(1, 489)       = {res}    25.82
{txt}       Model {c |} {res}  14.847212         1   14.847212   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 281.209814       489  .575071195   {txt}R-squared       ={res}    0.0501
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0482
{txt}       Total {c |} {res} 296.057026       490  .604198013   {txt}Root MSE        =   {res} .75833

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2} -.347821{col 31}{space 2} .0684533{col 42}{space 1}   -5.08{col 51}{space 3}0.000{col 59}{space 4}-.4823199{col 72}{space 3}-.2133222
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.086777{col 31}{space 2} .0487476{col 42}{space 1}   42.81{col 51}{space 3}0.000{col 59}{space 4} 1.990996{col 72}{space 3} 2.182557
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotRepublican
{txt}
{com}. 
. regress credibility i.condition if Republican==1 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       253
{txt}{hline 13}{c +}{hline 34}   F(1, 251)       = {res}    23.47
{txt}       Model {c |} {res} 15.6404258         1  15.6404258   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 167.268665       251  .666409025   {txt}R-squared       ={res}    0.0855
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0819
{txt}       Total {c |} {res} 182.909091       252  .725829726   {txt}Root MSE        =   {res} .81634

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.4986802{col 31}{space 2} .1029362{col 42}{space 1}   -4.84{col 51}{space 3}0.000{col 59}{space 4}-.7014091{col 72}{space 3}-.2959514
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.139706{col 31}{space 2} .0700005{col 42}{space 1}   30.57{col 51}{space 3}0.000{col 59}{space 4} 2.001843{col 72}{space 3} 2.277569
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Republican
{txt}
{com}. 
. coefplot NotRepublican Republican, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Republican), size(medium))
{res}{txt}
{com}. graph export "china_republican.pdf", replace
{txt}(file china_republican.pdf written in PDF format)

{com}. 
. *Conditional Effect of Voting in 2020 Presidential Election?
. regress credibility i.condition##i.Voting_Pres20 if condition==1 | condition==2

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       740
{txt}{hline 13}{c +}{hline 34}   F(3, 736)       = {res}    17.22
{txt}       Model {c |} {res} 31.0466393         3  10.3488798   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 442.331739       736  .600994211   {txt}R-squared       ={res}    0.0656
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0618
{txt}       Total {c |} {res} 473.378378       739  .640566141   {txt}Root MSE        =   {res} .77524

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            credibility{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      t{col 57}   P>|t|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}condition {c |}
{space 6}China/Commitment  {c |}{col 25}{res}{space 2} -.610119{col 37}{space 2} .1524885{col 48}{space 1}   -4.00{col 57}{space 3}0.000{col 65}{space 4}-.9094834{col 78}{space 3}-.3107547
{txt}{space 8}1.Voting_Pres20 {c |}{col 25}{res}{space 2}-.1948455{col 37}{space 2} .1197808{col 48}{space 1}   -1.63{col 57}{space 3}0.104{col 65}{space 4}-.4299983{col 78}{space 3} .0403073
{txt}{space 23} {c |}
condition#Voting_Pres20 {c |}
{space 4}China/Commitment#1  {c |}{col 25}{res}{space 2} .2442289{col 37}{space 2} .1644297{col 48}{space 1}    1.49{col 57}{space 3}0.138{col 65}{space 4}-.0785782{col 78}{space 3}  .567036
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} 2.270833{col 37}{space 2}  .111896{col 48}{space 1}   20.29{col 57}{space 3}0.000{col 65}{space 4}  2.05116{col 78}{space 3} 2.490507
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Voting_Pres20==0 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       104
{txt}{hline 13}{c +}{hline 34}   F(1, 102)       = {res}    17.51
{txt}       Model {c |} {res} 9.62110806         1  9.62110806   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 56.0327381       102   .54934057   {txt}R-squared       ={res}    0.1465
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1382
{txt}       Total {c |} {res} 65.6538462       103  .637415982   {txt}Root MSE        =   {res} .74118

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2} -.610119{col 31}{space 2} .1457884{col 42}{space 1}   -4.18{col 51}{space 3}0.000{col 59}{space 4}-.8992896{col 72}{space 3}-.3209485
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.270833{col 31}{space 2} .1069794{col 42}{space 1}   21.23{col 51}{space 3}0.000{col 59}{space 4}  2.05864{col 72}{space 3} 2.483026
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotVoting_Pres20
{txt}
{com}. 
. regress credibility i.condition if Voting_Pres20==1 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       636
{txt}{hline 13}{c +}{hline 34}   F(1, 634)       = {res}    34.89
{txt}       Model {c |} {res} 21.2607474         1  21.2607474   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 386.299001       634  .609304418   {txt}R-squared       ={res}    0.0522
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0507
{txt}       Total {c |} {res} 407.559748       635  .641826375   {txt}Root MSE        =   {res} .78058

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.3658901{col 31}{space 2} .0619411{col 42}{space 1}   -5.91{col 51}{space 3}0.000{col 59}{space 4}-.4875246{col 72}{space 3}-.2442557
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.075988{col 31}{space 2} .0430347{col 42}{space 1}   48.24{col 51}{space 3}0.000{col 59}{space 4}  1.99148{col 72}{space 3} 2.160496
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Voting_Pres20
{txt}
{com}. 
. coefplot NotVoting_Pres20 Voting_Pres20, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Voting), size(medium))
{res}{txt}
{com}. graph export "china_voting.pdf", replace
{txt}(file china_voting.pdf written in PDF format)

{com}. 
. *Conditional Effect of Income?
. sum Income

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}Income {c |}{res}      1,490    4.083221    2.190964          1          9
{txt}
{com}. gen High_Income=.
{txt}(1,515 missing values generated)

{com}. replace High_Income=1 if Income>6
{txt}(246 real changes made)

{com}. replace High_Income=0 if Income<=6
{txt}(1,269 real changes made)

{com}. tab High_Income

{txt}High_Income {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,269       83.76       83.76
{txt}          1 {c |}{res}        246       16.24      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,515      100.00
{txt}
{com}. 
. regress credibility i.condition##i.High_Income if condition==1 | condition==2

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       746
{txt}{hline 13}{c +}{hline 34}   F(3, 742)       = {res}    16.16
{txt}       Model {c |} {res} 29.4795149         3  9.82650498   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 451.138448       742  .608003299   {txt}R-squared       ={res}    0.0613
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0575
{txt}       Total {c |} {res} 480.617962       745  .645124782   {txt}Root MSE        =   {res} .77975

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          credibility{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}condition {c |}
{space 4}China/Commitment  {c |}{col 23}{res}{space 2}-.3942242{col 35}{space 2} .0620999{col 46}{space 1}   -6.35{col 55}{space 3}0.000{col 63}{space 4}-.5161367{col 76}{space 3}-.2723117
{txt}{space 8}1.High_Income {c |}{col 23}{res}{space 2}-.0159774{col 35}{space 2} .1128697{col 46}{space 1}   -0.14{col 55}{space 3}0.887{col 63}{space 4}-.2375594{col 76}{space 3} .2056045
{txt}{space 21} {c |}
condition#High_Income {c |}
{space 2}China/Commitment#1  {c |}{col 23}{res}{space 2}-.0170954{col 35}{space 2} .1581731{col 46}{space 1}   -0.11{col 55}{space 3}0.914{col 63}{space 4}-.3276156{col 76}{space 3} .2934248
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2} 2.105263{col 35}{space 2} .0433862{col 46}{space 1}   48.52{col 55}{space 3}0.000{col 63}{space 4} 2.020089{col 76}{space 3} 2.190437
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if High_Income==0 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       631
{txt}{hline 13}{c +}{hline 34}   F(1, 629)       = {res}    40.38
{txt}       Model {c |} {res}  24.502502         1   24.502502   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}  381.70352       629  .606841844   {txt}R-squared       ={res}    0.0603
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0588
{txt}       Total {c |} {res} 406.206022       630  .644771464   {txt}Root MSE        =   {res}   .779

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.3942242{col 31}{space 2} .0620406{col 42}{space 1}   -6.35{col 51}{space 3}0.000{col 59}{space 4}-.5160559{col 72}{space 3}-.2723925
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.105263{col 31}{space 2} .0433447{col 42}{space 1}   48.57{col 51}{space 3}0.000{col 59}{space 4} 2.020145{col 72}{space 3} 2.190381
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotHigh_Income
{txt}
{com}. 
. regress credibility i.condition if High_Income==1 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       115
{txt}{hline 13}{c +}{hline 34}   F(1, 113)       = {res}     7.91
{txt}       Model {c |} {res} 4.86072481         1  4.86072481   {txt}Prob > F        ={res}    0.0058
{txt}    Residual {c |} {res} 69.4349274       113  .614468384   {txt}R-squared       ={res}    0.0654
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0572
{txt}       Total {c |} {res} 74.2956522       114  .651716247   {txt}Root MSE        =   {res} .78388

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.4113196{col 31}{space 2} .1462442{col 42}{space 1}   -2.81{col 51}{space 3}0.006{col 59}{space 4}-.7010558{col 72}{space 3}-.1215835
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.089286{col 31}{space 2} .1047504{col 42}{space 1}   19.95{col 51}{space 3}0.000{col 59}{space 4} 1.881756{col 72}{space 3} 2.296815
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store High_Income
{txt}
{com}. 
. coefplot NotHigh_Income High_Income, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Income), size(medium))
{res}{txt}
{com}. graph export "china_income.pdf", replace
{txt}(file china_income.pdf written in PDF format)

{com}. 
. *Conditional Effect of Education?
. regress credibility i.condition##i.Education if condition==1 | condition==2

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       744
{txt}{hline 13}{c +}{hline 34}   F(3, 740)       = {res}    17.37
{txt}       Model {c |} {res} 31.5134236         3  10.5044745   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 447.452974       740  .604666181   {txt}R-squared       ={res}    0.0658
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0620
{txt}       Total {c |} {res} 478.966398       743   .64463849   {txt}Root MSE        =   {res}  .7776

{txt}{hline 46}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                                  credibility{col 47}{c |}      Coef.{col 59}   Std. Err.{col 71}      t{col 79}   P>|t|{col 87}     [95% Con{col 100}f. Interval]
{hline 46}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 36}condition {c |}
{space 28}China/Commitment  {c |}{col 47}{res}{space 2}-.4030315{col 59}{space 2} .0868541{col 70}{space 1}   -4.64{col 79}{space 3}0.000{col 87}{space 4}-.5735413{col 100}{space 3}-.2325216
{txt}{space 45} {c |}
{space 36}Education {c |}
{space 17}Bachelor’s degree or higher  {c |}{col 47}{res}{space 2} .1027506{col 59}{space 2} .0805901{col 70}{space 1}    1.27{col 79}{space 3}0.203{col 87}{space 4}-.0554619{col 100}{space 3} .2609631
{txt}{space 45} {c |}
{space 26}condition#Education {c |}
China/Commitment#Bachelor’s degree or higher  {c |}{col 47}{res}{space 2} .0056426{col 59}{space 2} .1151588{col 70}{space 1}    0.05{col 79}{space 3}0.961{col 87}{space 4}-.2204343{col 100}{space 3} .2317195
{txt}{space 45} {c |}
{space 40}_cons {c |}{col 47}{res}{space 2} 2.048193{col 59}{space 2} .0603537{col 70}{space 1}   33.94{col 79}{space 3}0.000{col 87}{space 4} 1.929708{col 100}{space 3} 2.166678
{txt}{hline 46}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Education==0 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       321
{txt}{hline 13}{c +}{hline 34}   F(1, 319)       = {res}    20.45
{txt}       Model {c |} {res} 13.0200513         1  13.0200513   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 203.098329       319  .636671877   {txt}R-squared       ={res}    0.0602
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0573
{txt}       Total {c |} {res}  216.11838       320  .675369938   {txt}Root MSE        =   {res} .79792

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.4030315{col 31}{space 2} .0891231{col 42}{space 1}   -4.52{col 51}{space 3}0.000{col 59}{space 4}-.5783749{col 72}{space 3}-.2276881
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.048193{col 31}{space 2} .0619304{col 42}{space 1}   33.07{col 51}{space 3}0.000{col 59}{space 4} 1.926349{col 72}{space 3} 2.170036
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotBachelor
{txt}
{com}. 
. regress credibility i.condition if Education==1 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       423
{txt}{hline 13}{c +}{hline 34}   F(1, 421)       = {res}    28.77
{txt}       Model {c |} {res} 16.6997281         1  16.6997281   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 244.354645       421  .580414835   {txt}R-squared       ={res}    0.0640
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0617
{txt}       Total {c |} {res} 261.054374       422   .61861226   {txt}Root MSE        =   {res} .76185

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.3973889{col 31}{space 2}  .074085{col 42}{space 1}   -5.36{col 51}{space 3}0.000{col 59}{space 4}-.5430115{col 72}{space 3}-.2517663
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.150943{col 31}{space 2}  .052324{col 42}{space 1}   41.11{col 51}{space 3}0.000{col 59}{space 4} 2.048094{col 72}{space 3} 2.253792
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Bachelor
{txt}
{com}. 
. coefplot NotBachelor Bachelor, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Education), size(medium))
{res}{txt}
{com}. graph export "china_education.pdf", replace
{txt}(file china_education.pdf written in PDF format)

{com}. 
. *Conditional Effect of Feeling toward China?
. sum Feeling_China

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
Feeling_Ch~a {c |}{res}      1,512    3.354497    1.466139          1          7
{txt}
{com}. gen Favorably_China=.
{txt}(1,515 missing values generated)

{com}. replace Favorably_China=1 if Feeling_China>5
{txt}(148 real changes made)

{com}. replace Favorably_China=0 if Feeling_China<=5
{txt}(1,367 real changes made)

{com}. tab Favorably_China

{txt}Favorably_C {c |}
       hina {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,367       90.23       90.23
{txt}          1 {c |}{res}        148        9.77      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,515      100.00
{txt}
{com}. 
. regress credibility i.condition##i.Favorably_China if condition==1 | condition==2

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       746
{txt}{hline 13}{c +}{hline 34}   F(3, 742)       = {res}    27.93
{txt}       Model {c |} {res} 48.7638682         3  16.2546227   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 431.854094       742  .582013604   {txt}R-squared       ={res}    0.1015
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0978
{txt}       Total {c |} {res} 480.617962       745  .645124782   {txt}Root MSE        =   {res}  .7629

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}              credibility{col 27}{c |}      Coef.{col 39}   Std. Err.{col 51}      t{col 59}   P>|t|{col 67}     [95% Con{col 80}f. Interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 16}condition {c |}
{space 8}China/Commitment  {c |}{col 27}{res}{space 2}   -.3675{col 39}{space 2} .0585989{col 50}{space 1}   -6.27{col 59}{space 3}0.000{col 67}{space 4}-.4825394{col 80}{space 3}-.2524607
{txt}{space 8}1.Favorably_China {c |}{col 27}{res}{space 2} .6460874{col 39}{space 2}  .130472{col 50}{space 1}    4.95{col 59}{space 3}0.000{col 67}{space 4} .3899492{col 80}{space 3} .9022255
{txt}{space 25} {c |}
condition#Favorably_China {c |}
{space 6}China/Commitment#1  {c |}{col 27}{res}{space 2}-.2167105{col 39}{space 2} .1953213{col 50}{space 1}   -1.11{col 59}{space 3}0.268{col 67}{space 4}-.6001588{col 80}{space 3} .1667378
{txt}{space 25} {c |}
{space 20}_cons {c |}{col 27}{res}{space 2} 2.038123{col 39}{space 2} .0413133{col 50}{space 1}   49.33{col 59}{space 3}0.000{col 67}{space 4} 1.957018{col 80}{space 3} 2.119228
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Favorably_China==0 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       678
{txt}{hline 13}{c +}{hline 34}   F(1, 676)       = {res}    39.58
{txt}       Model {c |} {res} 22.8912403         1  22.8912403   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 390.943568       676  .578318888   {txt}R-squared       ={res}    0.0553
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0539
{txt}       Total {c |} {res} 413.834808       677  .611277412   {txt}Root MSE        =   {res} .76047

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}   -.3675{col 31}{space 2} .0584126{col 42}{space 1}   -6.29{col 51}{space 3}0.000{col 59}{space 4}-.4821919{col 72}{space 3}-.2528081
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.038123{col 31}{space 2} .0411819{col 42}{space 1}   49.49{col 51}{space 3}0.000{col 59}{space 4} 1.957263{col 72}{space 3} 2.118983
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotFavorably_China
{txt}
{com}. 
. regress credibility i.condition if Favorably_China==1 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}        68
{txt}{hline 13}{c +}{hline 34}   F(1, 66)        = {res}     9.23
{txt}       Model {c |} {res} 5.72182663         1  5.72182663   {txt}Prob > F        ={res}    0.0034
{txt}    Residual {c |} {res} 40.9105263        66  .619856459   {txt}R-squared       ={res}    0.1227
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1094
{txt}       Total {c |} {res} 46.6323529        67  .696005268   {txt}Root MSE        =   {res} .78731

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.5842105{col 31}{space 2}  .192286{col 42}{space 1}   -3.04{col 51}{space 3}0.003{col 59}{space 4}-.9681219{col 72}{space 3}-.2002992
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.684211{col 31}{space 2} .1277185{col 42}{space 1}   21.02{col 51}{space 3}0.000{col 59}{space 4} 2.429212{col 72}{space 3} 2.939209
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Favorably_China
{txt}
{com}. 
. coefplot NotFavorably_China Favorably_China, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Feeling toward China), size(medium))
{res}{txt}
{com}. graph export "china_feeling.pdf", replace
{txt}(file china_feeling.pdf written in PDF format)

{com}. 
. *Conditional Effect of Knowledge on China?
. regress credibility i.condition##i.Knowledge_China if condition==1 | condition==2

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       746
{txt}{hline 13}{c +}{hline 34}   F(3, 742)       = {res}    17.78
{txt}       Model {c |} {res} 32.2389222         3  10.7463074   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}  448.37904       742  .604284421   {txt}R-squared       ={res}    0.0671
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0633
{txt}       Total {c |} {res} 480.617962       745  .645124782   {txt}Root MSE        =   {res} .77736

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}              credibility{col 27}{c |}      Coef.{col 39}   Std. Err.{col 51}      t{col 59}   P>|t|{col 67}     [95% Con{col 80}f. Interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 16}condition {c |}
{space 8}China/Commitment  {c |}{col 27}{res}{space 2}-.3168896{col 39}{space 2} .1149295{col 50}{space 1}   -2.76{col 59}{space 3}0.006{col 67}{space 4}-.5425154{col 80}{space 3}-.0912639
{txt}{space 8}1.Knowledge_China {c |}{col 27}{res}{space 2}-.0794198{col 39}{space 2} .0931334{col 50}{space 1}   -0.85{col 59}{space 3}0.394{col 67}{space 4}-.2622561{col 80}{space 3} .1034166
{txt}{space 25} {c |}
condition#Knowledge_China {c |}
{space 6}China/Commitment#1  {c |}{col 27}{res}{space 2}-.1073138{col 39}{space 2} .1323018{col 50}{space 1}   -0.81{col 59}{space 3}0.418{col 67}{space 4}-.3670441{col 80}{space 3} .1524166
{txt}{space 25} {c |}
{space 20}_cons {c |}{col 27}{res}{space 2} 2.163043{col 39}{space 2} .0810451{col 50}{space 1}   26.69{col 59}{space 3}0.000{col 67}{space 4} 2.003938{col 80}{space 3} 2.322148
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Knowledge_China==0 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       183
{txt}{hline 13}{c +}{hline 34}   F(1, 181)       = {res}     9.00
{txt}       Model {c |} {res} 4.59403385         1  4.59403385   {txt}Prob > F        ={res}    0.0031
{txt}    Residual {c |} {res} 92.4005017       181  .510500009   {txt}R-squared       ={res}    0.0474
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0421
{txt}       Total {c |} {res} 96.9945355       182  .532937008   {txt}Root MSE        =   {res} .71449

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.3168896{col 31}{space 2} .1056352{col 42}{space 1}   -3.00{col 51}{space 3}0.003{col 59}{space 4}-.5253245{col 72}{space 3}-.1084547
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.163043{col 31}{space 2}  .074491{col 42}{space 1}   29.04{col 51}{space 3}0.000{col 59}{space 4} 2.016061{col 72}{space 3} 2.310026
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotKnowledgable
{txt}
{com}. 
. regress credibility i.condition if Knowledge_China==1 & (condition==1 | condition==2)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       563
{txt}{hline 13}{c +}{hline 34}   F(1, 561)       = {res}    39.90
{txt}       Model {c |} {res} 25.3180866         1  25.3180866   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 355.978539       561   .63454285   {txt}R-squared       ={res}    0.0664
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0647
{txt}       Total {c |} {res} 381.296625       562  .678463746   {txt}Root MSE        =   {res} .79658

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
China/Commitment  {c |}{col 19}{res}{space 2}-.4242034{col 31}{space 2} .0671567{col 42}{space 1}   -6.32{col 51}{space 3}0.000{col 59}{space 4}-.5561127{col 72}{space 3}-.2922942
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.083624{col 31}{space 2} .0470207{col 42}{space 1}   44.31{col 51}{space 3}0.000{col 59}{space 4} 1.991265{col 72}{space 3} 2.175982
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Knowledgable
{txt}
{com}. 
. coefplot NotKnowledgable Knowledgable, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Knowledge (China)), size(medium))
{res}{txt}
{com}. graph export "china_knowledge.pdf", replace
{txt}(file china_knowledge.pdf written in PDF format)

{com}. 
. **Heterogenous Effect (Japan)
. *Conditional Effect of Gender?
. regress credibility i.condition##i.Male if condition==3 | condition==4

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       751
{txt}{hline 13}{c +}{hline 34}   F(3, 747)       = {res}     7.64
{txt}       Model {c |} {res} 10.3338795         3   3.4446265   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 336.600874       747   .45060358   {txt}R-squared       ={res}    0.0298
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0259
{txt}       Total {c |} {res} 346.934754       750  .462579672   {txt}Root MSE        =   {res} .67127

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        credibility{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}condition {c |}
{space 2}Japan/Commitment  {c |}{col 21}{res}{space 2}-.1322751{col 33}{space 2} .0690528{col 44}{space 1}   -1.92{col 53}{space 3}0.056{col 61}{space 4}-.2678358{col 74}{space 3} .0032855
{txt}{space 13}1.Male {c |}{col 21}{res}{space 2} .1957672{col 33}{space 2} .0690528{col 44}{space 1}    2.84{col 53}{space 3}0.005{col 61}{space 4} .0602065{col 74}{space 3} .3313279
{txt}{space 19} {c |}
{space 5}condition#Male {c |}
Japan/Commitment#1  {c |}{col 21}{res}{space 2}-.0587762{col 33}{space 2} .0979866{col 44}{space 1}   -0.60{col 53}{space 3}0.549{col 61}{space 4} -.251138{col 74}{space 3} .1335857
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 2.973545{col 33}{space 2} .0488277{col 44}{space 1}   60.90{col 53}{space 3}0.000{col 61}{space 4} 2.877689{col 74}{space 3} 3.069401
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Male==0 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       378
{txt}{hline 13}{c +}{hline 34}   F(1, 376)       = {res}     3.74
{txt}       Model {c |} {res} 1.65343915         1  1.65343915   {txt}Prob > F        ={res}    0.0538
{txt}    Residual {c |} {res}  166.10582       376  .441770798   {txt}R-squared       ={res}    0.0099
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0072
{txt}       Total {c |} {res} 167.759259       377  .444984773   {txt}Root MSE        =   {res} .66466

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.1322751{col 31}{space 2} .0683727{col 42}{space 1}   -1.93{col 51}{space 3}0.054{col 59}{space 4}-.2667159{col 72}{space 3} .0021656
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.973545{col 31}{space 2} .0483468{col 42}{space 1}   61.50{col 51}{space 3}0.000{col 59}{space 4} 2.878481{col 72}{space 3} 3.068609
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotMale
{txt}
{com}. 
. regress credibility i.condition if Male==1 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       373
{txt}{hline 13}{c +}{hline 34}   F(1, 371)       = {res}     7.41
{txt}       Model {c |} {res} 3.40306926         1  3.40306926   {txt}Prob > F        ={res}    0.0068
{txt}    Residual {c |} {res} 170.495054       371  .459555402   {txt}R-squared       ={res}    0.0196
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0169
{txt}       Total {c |} {res} 173.898123       372  .467468073   {txt}Root MSE        =   {res} .67791

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.1910513{col 31}{space 2} .0702075{col 42}{space 1}   -2.72{col 51}{space 3}0.007{col 59}{space 4}-.3291058{col 72}{space 3}-.0529967
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.169312{col 31}{space 2} .0493103{col 42}{space 1}   64.27{col 51}{space 3}0.000{col 59}{space 4} 3.072349{col 72}{space 3} 3.266275
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Male
{txt}
{com}. 
. coefplot NotMale Male, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Male), size(medium))
{res}{txt}
{com}. graph export "japan_male.pdf", replace
{txt}(file japan_male.pdf written in PDF format)

{com}. 
. *Conditional Effect of Age?
. regress credibility i.condition##i.Old if condition==3 | condition==4

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       761
{txt}{hline 13}{c +}{hline 34}   F(3, 757)       = {res}     5.12
{txt}       Model {c |} {res} 6.98013124         3  2.32671041   {txt}Prob > F        ={res}    0.0016
{txt}    Residual {c |} {res}  343.91343       757  .454311004   {txt}R-squared       ={res}    0.0199
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0160
{txt}       Total {c |} {res} 350.893561       760  .461702054   {txt}Root MSE        =   {res} .67403

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        credibility{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}condition {c |}
{space 2}Japan/Commitment  {c |}{col 21}{res}{space 2}-.1724138{col 33}{space 2} .0533699{col 44}{space 1}   -3.23{col 53}{space 3}0.001{col 61}{space 4}-.2771844{col 74}{space 3}-.0676432
{txt}{space 14}1.Old {c |}{col 21}{res}{space 2} .1054386{col 33}{space 2} .0929272{col 44}{space 1}    1.13{col 53}{space 3}0.257{col 61}{space 4}-.0769869{col 74}{space 3} .2878642
{txt}{space 19} {c |}
{space 6}condition#Old {c |}
Japan/Commitment#1  {c |}{col 21}{res}{space 2}  .047017{col 33}{space 2} .1327836{col 44}{space 1}    0.35{col 53}{space 3}0.723{col 61}{space 4}-.2136508{col 74}{space 3} .3076848
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 3.053292{col 33}{space 2} .0377382{col 44}{space 1}   80.91{col 53}{space 3}0.000{col 61}{space 4} 2.979208{col 74}{space 3} 3.127376
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Old==0 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       638
{txt}{hline 13}{c +}{hline 34}   F(1, 636)       = {res}    10.41
{txt}       Model {c |} {res} 4.74137931         1  4.74137931   {txt}Prob > F        ={res}    0.0013
{txt}    Residual {c |} {res} 289.567398       636  .455294651   {txt}R-squared       ={res}    0.0161
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0146
{txt}       Total {c |} {res} 294.308777       637  .462023198   {txt}Root MSE        =   {res} .67476

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.1724138{col 31}{space 2} .0534276{col 42}{space 1}   -3.23{col 51}{space 3}0.001{col 59}{space 4}-.2773297{col 72}{space 3}-.0674979
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.053292{col 31}{space 2}  .037779{col 42}{space 1}   80.82{col 51}{space 3}0.000{col 59}{space 4} 2.979105{col 72}{space 3} 3.127478
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotOld
{txt}
{com}. 
. regress credibility i.condition if Old==1 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       123
{txt}{hline 13}{c +}{hline 34}   F(1, 121)       = {res}     1.08
{txt}       Model {c |} {res} .483236547         1  .483236547   {txt}Prob > F        ={res}    0.3017
{txt}    Residual {c |} {res} 54.3460317       121  .449140758   {txt}R-squared       ={res}    0.0088
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0006
{txt}       Total {c |} {res} 54.8292683       122  .449420232   {txt}Root MSE        =   {res} .67018

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.1253968{col 31}{space 2} .1208921{col 42}{space 1}   -1.04{col 51}{space 3}0.302{col 59}{space 4}-.3647346{col 72}{space 3}  .113941
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}  3.15873{col 31}{space 2} .0844347{col 42}{space 1}   37.41{col 51}{space 3}0.000{col 59}{space 4} 2.991569{col 72}{space 3} 3.325891
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Old
{txt}
{com}. 
. coefplot NotOld Old, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Old), size(medium))
{res}{txt}
{com}. graph export "japan_old.pdf", replace
{txt}(file japan_old.pdf written in PDF format)

{com}. 
. *Conditional Effect of White?
. regress credibility i.condition##i.White if condition==3 | condition==4

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       757
{txt}{hline 13}{c +}{hline 34}   F(3, 753)       = {res}     5.48
{txt}       Model {c |} {res} 7.47384812         3  2.49128271   {txt}Prob > F        ={res}    0.0010
{txt}    Residual {c |} {res} 342.394051       753  .454706576   {txt}R-squared       ={res}    0.0214
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0175
{txt}       Total {c |} {res}   349.8679       756  .462788227   {txt}Root MSE        =   {res} .67432

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            credibility{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      t{col 57}   P>|t|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}condition {c |}
{space 6}Japan/Commitment  {c |}{col 25}{res}{space 2}-.2475075{col 37}{space 2} .0848344{col 48}{space 1}   -2.92{col 57}{space 3}0.004{col 65}{space 4}-.4140475{col 78}{space 3}-.0809674
{txt}{space 23} {c |}
{space 18}White {c |}
{space 17}White  {c |}{col 25}{res}{space 2} .0419479{col 37}{space 2} .0721594{col 48}{space 1}    0.58{col 57}{space 3}0.561{col 65}{space 4}-.0997096{col 78}{space 3} .1836055
{txt}{space 23} {c |}
{space 8}condition#White {c |}
Japan/Commitment#White  {c |}{col 25}{res}{space 2} .1112489{col 37}{space 2} .1040002{col 48}{space 1}    1.07{col 57}{space 3}0.285{col 65}{space 4}-.0929161{col 78}{space 3} .3154138
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} 3.044118{col 37}{space 2} .0578224{col 48}{space 1}   52.65{col 57}{space 3}0.000{col 65}{space 4} 2.930605{col 78}{space 3}  3.15763
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if White==0 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       254
{txt}{hline 13}{c +}{hline 34}   F(1, 252)       = {res}     7.45
{txt}       Model {c |} {res} 3.87047126         1  3.87047126   {txt}Prob > F        ={res}    0.0068
{txt}    Residual {c |} {res} 130.853938       252  .519261659   {txt}R-squared       ={res}    0.0287
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0249
{txt}       Total {c |} {res} 134.724409       253  .532507547   {txt}Root MSE        =   {res}  .7206

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.2475075{col 31}{space 2} .0906566{col 42}{space 1}   -2.73{col 51}{space 3}0.007{col 59}{space 4}-.4260487{col 72}{space 3}-.0689663
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.044118{col 31}{space 2} .0617908{col 42}{space 1}   49.26{col 51}{space 3}0.000{col 59}{space 4} 2.922426{col 72}{space 3}  3.16581
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotWhite
{txt}
{com}. 
. regress credibility i.condition if White==1 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       503
{txt}{hline 13}{c +}{hline 34}   F(1, 501)       = {res}     5.52
{txt}       Model {c |} {res} 2.33265012         1  2.33265012   {txt}Prob > F        ={res}    0.0191
{txt}    Residual {c |} {res} 211.540113       501  .422235755   {txt}R-squared       ={res}    0.0109
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0089
{txt}       Total {c |} {res} 213.872763       502  .426041361   {txt}Root MSE        =   {res}  .6498

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.1362586{col 31}{space 2} .0579718{col 42}{space 1}   -2.35{col 51}{space 3}0.019{col 59}{space 4}-.2501564{col 72}{space 3}-.0223609
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.086066{col 31}{space 2}  .041599{col 42}{space 1}   74.19{col 51}{space 3}0.000{col 59}{space 4} 3.004336{col 72}{space 3} 3.167796
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store White
{txt}
{com}. 
. coefplot NotWhite White, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (White), size(medium))
{res}{txt}
{com}. graph export "japan_white.pdf", replace
{txt}(file japan_white.pdf written in PDF format)

{com}. 
. *Conditional Effect of Ideology?
. regress credibility i.condition##i.Liberal if condition==3 | condition==4

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       761
{txt}{hline 13}{c +}{hline 34}   F(3, 757)       = {res}     4.26
{txt}       Model {c |} {res} 5.82544658         3  1.94181553   {txt}Prob > F        ={res}    0.0054
{txt}    Residual {c |} {res} 345.068115       757  .455836347   {txt}R-squared       ={res}    0.0166
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0127
{txt}       Total {c |} {res} 350.893561       760  .461702054   {txt}Root MSE        =   {res} .67516

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        credibility{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}condition {c |}
{space 2}Japan/Commitment  {c |}{col 21}{res}{space 2}-.1333415{col 33}{space 2} .0597947{col 44}{space 1}   -2.23{col 53}{space 3}0.026{col 61}{space 4}-.2507247{col 74}{space 3}-.0159584
{txt}{space 10}1.Liberal {c |}{col 21}{res}{space 2} .0138521{col 33}{space 2} .0736232{col 44}{space 1}    0.19{col 53}{space 3}0.851{col 61}{space 4}-.1306777{col 74}{space 3}  .158382
{txt}{space 19} {c |}
{space 2}condition#Liberal {c |}
Japan/Commitment#1  {c |}{col 21}{res}{space 2}-.0974521{col 33}{space 2} .1041146{col 44}{space 1}   -0.94{col 53}{space 3}0.350{col 61}{space 4}-.3018397{col 74}{space 3} .1069355
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 3.066148{col 33}{space 2} .0421151{col 44}{space 1}   72.80{col 53}{space 3}0.000{col 61}{space 4} 2.983472{col 74}{space 3} 3.148824
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Liberal==0 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       510
{txt}{hline 13}{c +}{hline 34}   F(1, 508)       = {res}     4.72
{txt}       Model {c |} {res} 2.26680611         1  2.26680611   {txt}Prob > F        ={res}    0.0302
{txt}    Residual {c |} {res} 243.733194       508  .479789752   {txt}R-squared       ={res}    0.0092
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0073
{txt}       Total {c |} {res}        246       509  .483300589   {txt}Root MSE        =   {res} .69267

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.1333415{col 31}{space 2} .0613456{col 42}{space 1}   -2.17{col 51}{space 3}0.030{col 59}{space 4}-.2538639{col 72}{space 3}-.0128191
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.066148{col 31}{space 2} .0432075{col 42}{space 1}   70.96{col 51}{space 3}0.000{col 59}{space 4} 2.981261{col 72}{space 3} 3.151035
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotLiberal
{txt}
{com}. 
. regress credibility i.condition if Liberal==1 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       251
{txt}{hline 13}{c +}{hline 34}   F(1, 249)       = {res}     8.21
{txt}       Model {c |} {res}  3.3423702         1   3.3423702   {txt}Prob > F        ={res}    0.0045
{txt}    Residual {c |} {res} 101.334921       249  .406967553   {txt}R-squared       ={res}    0.0319
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0280
{txt}       Total {c |} {res} 104.677291       250  .418709163   {txt}Root MSE        =   {res} .63794

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.2307937{col 31}{space 2} .0805335{col 42}{space 1}   -2.87{col 51}{space 3}0.005{col 59}{space 4}-.3894073{col 72}{space 3}  -.07218
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}     3.08{col 31}{space 2} .0570591{col 42}{space 1}   53.98{col 51}{space 3}0.000{col 59}{space 4}  2.96762{col 72}{space 3}  3.19238
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Liberal
{txt}
{com}. 
. coefplot NotLiberal Liberal, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Ideology), size(medium))
{res}{txt}
{com}. graph export "japan_liberal.pdf", replace
{txt}(file japan_liberal.pdf written in PDF format)

{com}. 
. *Conditional Effect of Democrat?
. regress credibility i.condition##i.Democrat if condition==3 | condition==4

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       759
{txt}{hline 13}{c +}{hline 34}   F(3, 755)       = {res}     4.28
{txt}       Model {c |} {res} 5.79041713         3  1.93013904   {txt}Prob > F        ={res}    0.0052
{txt}    Residual {c |} {res} 340.125261       755  .450497035   {txt}R-squared       ={res}    0.0167
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0128
{txt}       Total {c |} {res} 345.915679       758  .456353138   {txt}Root MSE        =   {res} .67119

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}               credibility{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}condition {c |}
{space 9}Japan/Commitment  {c |}{col 28}{res}{space 2}-.1970971{col 40}{space 2} .0614863{col 51}{space 1}   -3.21{col 60}{space 3}0.001{col 68}{space 4}-.3178015{col 81}{space 3}-.0763927
{txt}{space 26} {c |}
{space 18}Democrat {c |}
{space 17}Democrat  {c |}{col 28}{res}{space 2}-.0653541{col 40}{space 2} .0706095{col 51}{space 1}   -0.93{col 60}{space 3}0.355{col 68}{space 4}-.2039684{col 81}{space 3} .0732601
{txt}{space 26} {c |}
{space 8}condition#Democrat {c |}
Japan/Commitment#Democrat  {c |}{col 28}{res}{space 2} .0737611{col 40}{space 2} .1009276{col 51}{space 1}    0.73{col 60}{space 3}0.465{col 68}{space 4} -.124371{col 81}{space 3} .2718932
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 3.099138{col 40}{space 2} .0440658{col 51}{space 1}   70.33{col 60}{space 3}0.000{col 68}{space 4} 3.012632{col 81}{space 3} 3.185644
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Democrat==0 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       477
{txt}{hline 13}{c +}{hline 34}   F(1, 475)       = {res}     9.71
{txt}       Model {c |} {res} 4.62909639         1  4.62909639   {txt}Prob > F        ={res}    0.0019
{txt}    Residual {c |} {res} 226.368807       475   .47656591   {txt}R-squared       ={res}    0.0200
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0180
{txt}       Total {c |} {res} 230.997904       476  .485289713   {txt}Root MSE        =   {res} .69034

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.1970971{col 31}{space 2} .0632403{col 42}{space 1}   -3.12{col 51}{space 3}0.002{col 59}{space 4}-.3213624{col 72}{space 3}-.0728319
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.099138{col 31}{space 2} .0453229{col 42}{space 1}   68.38{col 51}{space 3}0.000{col 59}{space 4}  3.01008{col 72}{space 3} 3.188196
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotDemocrat
{txt}
{com}. 
. regress credibility i.condition if Democrat==1 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       282
{txt}{hline 13}{c +}{hline 34}   F(1, 280)       = {res}     2.63
{txt}       Model {c |} {res} 1.06978692         1  1.06978692   {txt}Prob > F        ={res}    0.1058
{txt}    Residual {c |} {res} 113.756454       280  .406273051   {txt}R-squared       ={res}    0.0093
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0058
{txt}       Total {c |} {res} 114.826241       281   .40863431   {txt}Root MSE        =   {res}  .6374

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2} -.123336{col 31}{space 2} .0760064{col 42}{space 1}   -1.62{col 51}{space 3}0.106{col 59}{space 4}-.2729526{col 72}{space 3} .0262805
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.033784{col 31}{space 2} .0523936{col 42}{space 1}   57.90{col 51}{space 3}0.000{col 59}{space 4} 2.930648{col 72}{space 3} 3.136919
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Democrat
{txt}
{com}. 
. coefplot NotDemocrat Democrat, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Democrat), size(medium))
{res}{txt}
{com}. graph export "japan_democrat.pdf", replace
{txt}(file japan_democrat.pdf written in PDF format)

{com}. 
. *Conditional Effect of Republican?
. regress credibility i.condition##i.Republican if condition==3 | condition==4

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       759
{txt}{hline 13}{c +}{hline 34}   F(3, 755)       = {res}     4.05
{txt}       Model {c |} {res} 5.47304988         3  1.82434996   {txt}Prob > F        ={res}    0.0072
{txt}    Residual {c |} {res} 340.442629       755  .450917389   {txt}R-squared       ={res}    0.0158
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0119
{txt}       Total {c |} {res} 345.915679       758  .456353138   {txt}Root MSE        =   {res}  .6715

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 credibility{col 30}{c |}      Coef.{col 42}   Std. Err.{col 54}      t{col 62}   P>|t|{col 70}     [95% Con{col 83}f. Interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}condition {c |}
{space 11}Japan/Commitment  {c |}{col 30}{res}{space 2}-.1569482{col 42}{space 2} .0579672{col 53}{space 1}   -2.71{col 62}{space 3}0.007{col 70}{space 4}-.2707443{col 83}{space 3}-.0431521
{txt}{space 28} {c |}
{space 18}Republican {c |}
{space 17}Republican  {c |}{col 30}{res}{space 2} .0286462{col 42}{space 2}  .076809{col 53}{space 1}    0.37{col 62}{space 3}0.709{col 70}{space 4}-.1221384{col 83}{space 3} .1794308
{txt}{space 28} {c |}
{space 8}condition#Republican {c |}
Japan/Commitment#Republican  {c |}{col 30}{res}{space 2}-.0408397{col 42}{space 2} .1072444{col 53}{space 1}   -0.38{col 62}{space 3}0.703{col 70}{space 4}-.2513723{col 83}{space 3} .1696929
{txt}{space 28} {c |}
{space 23}_cons {c |}{col 30}{res}{space 2} 3.065693{col 42}{space 2}  .040567{col 53}{space 1}   75.57{col 62}{space 3}0.000{col 70}{space 4} 2.986056{col 83}{space 3} 3.145331
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Republican==0 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       537
{txt}{hline 13}{c +}{hline 34}   F(1, 535)       = {res}     7.87
{txt}       Model {c |} {res} 3.30555671         1  3.30555671   {txt}Prob > F        ={res}    0.0052
{txt}    Residual {c |} {res} 224.627404       535  .419864307   {txt}R-squared       ={res}    0.0145
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0127
{txt}       Total {c |} {res} 227.932961       536  .425248061   {txt}Root MSE        =   {res} .64797

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.1569482{col 31}{space 2} .0559356{col 42}{space 1}   -2.81{col 51}{space 3}0.005{col 59}{space 4}-.2668286{col 72}{space 3}-.0470678
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.065693{col 31}{space 2} .0391453{col 42}{space 1}   78.32{col 51}{space 3}0.000{col 59}{space 4} 2.988796{col 72}{space 3} 3.142591
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotRepublican
{txt}
{com}. 
. regress credibility i.condition if Republican==1 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       222
{txt}{hline 13}{c +}{hline 34}   F(1, 220)       = {res}     4.12
{txt}       Model {c |} {res} 2.16675752         1  2.16675752   {txt}Prob > F        ={res}    0.0437
{txt}    Residual {c |} {res} 115.815224       220  .526432838   {txt}R-squared       ={res}    0.0184
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0139
{txt}       Total {c |} {res} 117.981982       221  .533855122   {txt}Root MSE        =   {res} .72556

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.1977879{col 31}{space 2} .0974913{col 42}{space 1}   -2.03{col 51}{space 3}0.044{col 59}{space 4}-.3899244{col 72}{space 3}-.0056514
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}  3.09434{col 31}{space 2} .0704723{col 42}{space 1}   43.91{col 51}{space 3}0.000{col 59}{space 4} 2.955452{col 72}{space 3} 3.233227
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Republican
{txt}
{com}. 
. coefplot NotRepublican Republican, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Republican), size(medium))
{res}{txt}
{com}. graph export "japan_republican.pdf", replace
{txt}(file japan_republican.pdf written in PDF format)

{com}. 
. *Conditional Effect of Voting in 2020 Presidential Election?
. regress credibility i.condition##i.Voting_Pres20 if condition==3 | condition==4

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       756
{txt}{hline 13}{c +}{hline 34}   F(3, 752)       = {res}     5.06
{txt}       Model {c |} {res} 6.92904159         3  2.30968053   {txt}Prob > F        ={res}    0.0018
{txt}    Residual {c |} {res} 342.938683       752  .456035483   {txt}R-squared       ={res}    0.0198
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0159
{txt}       Total {c |} {res} 349.867725       755   .46340096   {txt}Root MSE        =   {res}  .6753

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            credibility{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      t{col 57}   P>|t|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}condition {c |}
{space 6}Japan/Commitment  {c |}{col 25}{res}{space 2} .0746415{col 37}{space 2} .1284497{col 48}{space 1}    0.58{col 57}{space 3}0.561{col 65}{space 4}-.1775212{col 78}{space 3} .3268041
{txt}{space 8}1.Voting_Pres20 {c |}{col 25}{res}{space 2} .1466494{col 37}{space 2} .1008404{col 48}{space 1}    1.45{col 57}{space 3}0.146{col 65}{space 4}-.0513129{col 78}{space 3} .3446116
{txt}{space 23} {c |}
condition#Voting_Pres20 {c |}
{space 4}Japan/Commitment#1  {c |}{col 25}{res}{space 2} -.279586{col 37}{space 2} .1390244{col 48}{space 1}   -2.01{col 57}{space 3}0.045{col 65}{space 4}-.5525081{col 78}{space 3}-.0066639
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} 2.942308{col 37}{space 2} .0936478{col 48}{space 1}   31.42{col 57}{space 3}0.000{col 65}{space 4} 2.758465{col 78}{space 3}  3.12615
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Voting_Pres20==0 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       111
{txt}{hline 13}{c +}{hline 34}   F(1, 109)       = {res}     0.31
{txt}       Model {c |} {res}  .15399004         1   .15399004   {txt}Prob > F        ={res}    0.5776
{txt}    Residual {c |} {res} 53.8099739       109  .493669486   {txt}R-squared       ={res}    0.0029
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0063
{txt}       Total {c |} {res}  53.963964       110  .490581491   {txt}Root MSE        =   {res} .70262

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2} .0746415{col 31}{space 2} .1336448{col 42}{space 1}    0.56{col 51}{space 3}0.578{col 59}{space 4}-.1902381{col 72}{space 3}  .339521
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.942308{col 31}{space 2} .0974353{col 42}{space 1}   30.20{col 51}{space 3}0.000{col 59}{space 4} 2.749194{col 72}{space 3} 3.135421
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotVoting_Pres20
{txt}
{com}. 
. regress credibility i.condition if Voting_Pres20==1 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       645
{txt}{hline 13}{c +}{hline 34}   F(1, 643)       = {res}    15.06
{txt}       Model {c |} {res} 6.77206585         1  6.77206585   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 289.128709       643  .449655847   {txt}R-squared       ={res}    0.0229
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0214
{txt}       Total {c |} {res} 295.900775       644  .459473253   {txt}Root MSE        =   {res} .67056

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.2049445{col 31}{space 2}   .05281{col 42}{space 1}   -3.88{col 51}{space 3}0.000{col 59}{space 4}-.3086453{col 72}{space 3}-.1012437
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.088957{col 31}{space 2} .0371391{col 42}{space 1}   83.17{col 51}{space 3}0.000{col 59}{space 4} 3.016028{col 72}{space 3} 3.161886
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Voting_Pres20
{txt}
{com}. 
. coefplot NotVoting_Pres20 Voting_Pres20, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Voting), size(medium))
{res}{txt}
{com}. graph export "japan_voting.pdf", replace
{txt}(file japan_voting.pdf written in PDF format)

{com}. 
. *Conditional Effect of Income?
. regress credibility i.condition##i.High_Income if condition==3 | condition==4

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       761
{txt}{hline 13}{c +}{hline 34}   F(3, 757)       = {res}     4.66
{txt}       Model {c |} {res} 6.36731532         3  2.12243844   {txt}Prob > F        ={res}    0.0031
{txt}    Residual {c |} {res} 344.526246       757  .455120536   {txt}R-squared       ={res}    0.0181
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0143
{txt}       Total {c |} {res} 350.893561       760  .461702054   {txt}Root MSE        =   {res} .67463

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          credibility{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}condition {c |}
{space 4}Japan/Commitment  {c |}{col 23}{res}{space 2}-.1331826{col 35}{space 2}  .053713{col 46}{space 1}   -2.48{col 55}{space 3}0.013{col 63}{space 4}-.2386267{col 76}{space 3}-.0277386
{txt}{space 8}1.High_Income {c |}{col 23}{res}{space 2} .1343498{col 35}{space 2} .0913018{col 46}{space 1}    1.47{col 55}{space 3}0.142{col 63}{space 4} -.044885{col 76}{space 3} .3135847
{txt}{space 21} {c |}
condition#High_Income {c |}
{space 2}Japan/Commitment#1  {c |}{col 23}{res}{space 2}-.1892605{col 35}{space 2} .1299696{col 46}{space 1}   -1.46{col 55}{space 3}0.146{col 63}{space 4}-.4444043{col 76}{space 3} .0658832
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2} 3.047468{col 35}{space 2} .0379507{col 46}{space 1}   80.30{col 55}{space 3}0.000{col 63}{space 4} 2.972967{col 76}{space 3} 3.121969
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if High_Income==0 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       631
{txt}{hline 13}{c +}{hline 34}   F(1, 629)       = {res}     5.97
{txt}       Model {c |} {res} 2.79810184         1  2.79810184   {txt}Prob > F        ={res}    0.0149
{txt}    Residual {c |} {res} 294.973689       629   .46895658   {txt}R-squared       ={res}    0.0094
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0078
{txt}       Total {c |} {res} 297.771791       630  .472653636   {txt}Root MSE        =   {res}  .6848

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.1331826{col 31}{space 2} .0545233{col 42}{space 1}   -2.44{col 51}{space 3}0.015{col 59}{space 4}-.2402524{col 72}{space 3}-.0261129
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.047468{col 31}{space 2} .0385232{col 42}{space 1}   79.11{col 51}{space 3}0.000{col 59}{space 4} 2.971819{col 72}{space 3} 3.123118
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotHigh_Income
{txt}
{com}. 
. regress credibility i.condition if High_Income==1 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       130
{txt}{hline 13}{c +}{hline 34}   F(1, 128)       = {res}     8.73
{txt}       Model {c |} {res} 3.37821241         1  3.37821241   {txt}Prob > F        ={res}    0.0037
{txt}    Residual {c |} {res} 49.5525568       128   .38712935   {txt}R-squared       ={res}    0.0638
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0565
{txt}       Total {c |} {res} 52.9307692       129  .410316041   {txt}Root MSE        =   {res}  .6222

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.3224432{col 31}{space 2} .1091535{col 42}{space 1}   -2.95{col 51}{space 3}0.004{col 59}{space 4}-.5384221{col 72}{space 3}-.1064643
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.181818{col 31}{space 2} .0765872{col 42}{space 1}   41.55{col 51}{space 3}0.000{col 59}{space 4} 3.030277{col 72}{space 3} 3.333359
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store High_Income
{txt}
{com}. 
. coefplot NotHigh_Income High_Income, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Income), size(medium))
{res}{txt}
{com}. graph export "japan_income.pdf", replace
{txt}(file japan_income.pdf written in PDF format)

{com}. 
. *Conditional Effect of Education?
. regress credibility i.condition##i.Education if condition==3 | condition==4

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       759
{txt}{hline 13}{c +}{hline 34}   F(3, 755)       = {res}     4.42
{txt}       Model {c |} {res} 6.02648307         3  2.00882769   {txt}Prob > F        ={res}    0.0043
{txt}    Residual {c |} {res} 342.866798       755  .454128209   {txt}R-squared       ={res}    0.0173
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0134
{txt}       Total {c |} {res} 348.893281       758  .460281373   {txt}Root MSE        =   {res} .67389

{txt}{hline 46}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                                  credibility{col 47}{c |}      Coef.{col 59}   Std. Err.{col 71}      t{col 79}   P>|t|{col 87}     [95% Con{col 100}f. Interval]
{hline 46}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 36}condition {c |}
{space 28}Japan/Commitment  {c |}{col 47}{res}{space 2} -.114565{col 59}{space 2} .0780819{col 70}{space 1}   -1.47{col 79}{space 3}0.143{col 87}{space 4}-.2678484{col 100}{space 3} .0387184
{txt}{space 45} {c |}
{space 36}Education {c |}
{space 17}Bachelor’s degree or higher  {c |}{col 47}{res}{space 2}-.0067448{col 59}{space 2} .0708644{col 70}{space 1}   -0.10{col 79}{space 3}0.924{col 87}{space 4}-.1458596{col 100}{space 3} .1323699
{txt}{space 45} {c |}
{space 26}condition#Education {c |}
Japan/Commitment#Bachelor’s degree or higher  {c |}{col 47}{res}{space 2}-.0862634{col 59}{space 2} .1001931{col 70}{space 1}   -0.86{col 79}{space 3}0.390{col 87}{space 4}-.2829536{col 100}{space 3} .1104267
{txt}{space 45} {c |}
{space 40}_cons {c |}{col 47}{res}{space 2}  3.07483{col 59}{space 2} .0555815{col 70}{space 1}   55.32{col 79}{space 3}0.000{col 87}{space 4} 2.965717{col 100}{space 3} 3.183943
{txt}{hline 46}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Education==0 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       298
{txt}{hline 13}{c +}{hline 34}   F(1, 296)       = {res}     2.01
{txt}       Model {c |} {res}  .97764723         1   .97764723   {txt}Prob > F        ={res}    0.1573
{txt}    Residual {c |} {res}  143.93846       296  .486278582   {txt}R-squared       ={res}    0.0067
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0034
{txt}       Total {c |} {res} 144.916107       297  .487933021   {txt}Root MSE        =   {res} .69734

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2} -.114565{col 31}{space 2} .0807985{col 42}{space 1}   -1.42{col 51}{space 3}0.157{col 59}{space 4}-.2735774{col 72}{space 3} .0444474
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}  3.07483{col 31}{space 2} .0575154{col 42}{space 1}   53.46{col 51}{space 3}0.000{col 59}{space 4} 2.961639{col 72}{space 3} 3.188021
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotBachelor
{txt}
{com}. 
. regress credibility i.condition if Education==1 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       461
{txt}{hline 13}{c +}{hline 34}   F(1, 459)       = {res}    10.72
{txt}       Model {c |} {res}  4.6464999         1   4.6464999   {txt}Prob > F        ={res}    0.0011
{txt}    Residual {c |} {res} 198.928337       459  .433395071   {txt}R-squared       ={res}    0.0228
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0207
{txt}       Total {c |} {res} 203.574837       460  .442553994   {txt}Root MSE        =   {res} .65833

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.2008285{col 31}{space 2} .0613344{col 42}{space 1}   -3.27{col 51}{space 3}0.001{col 59}{space 4}-.3213596{col 72}{space 3}-.0802974
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.068085{col 31}{space 2} .0429445{col 42}{space 1}   71.44{col 51}{space 3}0.000{col 59}{space 4} 2.983693{col 72}{space 3} 3.152477
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Bachelor
{txt}
{com}. 
. coefplot NotBachelor Bachelor, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Education), size(medium))
{res}{txt}
{com}. graph export "japan_education.pdf", replace
{txt}(file japan_education.pdf written in PDF format)

{com}. 
. *Conditional Effect of Feeling toward Japan?
. sum Feeling_Japan

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
Feeling_Ja~n {c |}{res}      1,514    5.471598    1.260325          1          7
{txt}
{com}. gen Favorably_Japan=.
{txt}(1,515 missing values generated)

{com}. replace Favorably_Japan=1 if Feeling_Japan>5
{txt}(846 real changes made)

{com}. replace Favorably_Japan=0 if Feeling_Japan<=5
{txt}(669 real changes made)

{com}. tab Favorably_Japan

{txt}Favorably_J {c |}
       apan {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        669       44.16       44.16
{txt}          1 {c |}{res}        846       55.84      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,515      100.00
{txt}
{com}. 
. regress credibility i.condition##i.Favorably_Japan if condition==3 | condition==4

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       761
{txt}{hline 13}{c +}{hline 34}   F(3, 757)       = {res}    28.51
{txt}       Model {c |} {res}  35.624079         3   11.874693   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 315.269482       757  .416472235   {txt}R-squared       ={res}    0.1015
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0980
{txt}       Total {c |} {res} 350.893561       760  .461702054   {txt}Root MSE        =   {res} .64535

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}              credibility{col 27}{c |}      Coef.{col 39}   Std. Err.{col 51}      t{col 59}   P>|t|{col 67}     [95% Con{col 80}f. Interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 16}condition {c |}
{space 8}Japan/Commitment  {c |}{col 27}{res}{space 2}-.0186621{col 39}{space 2} .0710555{col 50}{space 1}   -0.26{col 59}{space 3}0.793{col 67}{space 4}-.1581514{col 80}{space 3} .1208273
{txt}{space 8}1.Favorably_Japan {c |}{col 27}{res}{space 2} .5085301{col 39}{space 2} .0667589{col 50}{space 1}    7.62{col 59}{space 3}0.000{col 67}{space 4} .3774756{col 80}{space 3} .6395846
{txt}{space 25} {c |}
condition#Favorably_Japan {c |}
{space 6}Japan/Commitment#1  {c |}{col 27}{res}{space 2}-.2501412{col 39}{space 2} .0944197{col 50}{space 1}   -2.65{col 59}{space 3}0.008{col 67}{space 4}-.4354968{col 80}{space 3}-.0647857
{txt}{space 25} {c |}
{space 20}_cons {c |}{col 27}{res}{space 2} 2.779141{col 39}{space 2} .0505474{col 50}{space 1}   54.98{col 59}{space 3}0.000{col 67}{space 4} 2.679911{col 80}{space 3} 2.878371
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Favorably_Japan==0 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       330
{txt}{hline 13}{c +}{hline 34}   F(1, 328)       = {res}     0.07
{txt}       Model {c |} {res} .028728266         1  .028728266   {txt}Prob > F        ={res}    0.7972
{txt}    Residual {c |} {res} 142.468241       328  .434354395   {txt}R-squared       ={res}    0.0002
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0028
{txt}       Total {c |} {res}  142.49697       329  .433121488   {txt}Root MSE        =   {res} .65906

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.0186621{col 31}{space 2}  .072565{col 42}{space 1}   -0.26{col 51}{space 3}0.797{col 59}{space 4}-.1614135{col 72}{space 3} .1240894
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.779141{col 31}{space 2} .0516212{col 42}{space 1}   53.84{col 51}{space 3}0.000{col 59}{space 4} 2.677591{col 72}{space 3} 2.880692
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotFavorably_Japan
{txt}
{com}. 
. regress credibility i.condition if Favorably_Japan==1 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       431
{txt}{hline 13}{c +}{hline 34}   F(1, 429)       = {res}    19.32
{txt}       Model {c |} {res} 7.78344614         1  7.78344614   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 172.801241       429  .402800095   {txt}R-squared       ={res}    0.0431
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0409
{txt}       Total {c |} {res} 180.584687       430  .419964388   {txt}Root MSE        =   {res} .63467

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.2688033{col 31}{space 2} .0611496{col 42}{space 1}   -4.40{col 51}{space 3}0.000{col 59}{space 4}-.3889934{col 72}{space 3}-.1486133
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.287671{col 31}{space 2} .0428867{col 42}{space 1}   76.66{col 51}{space 3}0.000{col 59}{space 4} 3.203377{col 72}{space 3} 3.371965
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Favorably_Japan
{txt}
{com}. 
. coefplot NotFavorably_Japan Favorably_Japan, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Feeling toward Japan), size(medium))
{res}{txt}
{com}. graph export "japan_feeling.pdf", replace
{txt}(file japan_feeling.pdf written in PDF format)

{com}. 
. *Conditional Effect of Knowledge on Japan?
. regress credibility i.condition##i.Knowledge_Japan if condition==3 | condition==4

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       761
{txt}{hline 13}{c +}{hline 34}   F(3, 757)       = {res}     4.08
{txt}       Model {c |} {res} 5.57941925         3  1.85980642   {txt}Prob > F        ={res}    0.0069
{txt}    Residual {c |} {res} 345.314142       757   .45616135   {txt}R-squared       ={res}    0.0159
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0120
{txt}       Total {c |} {res} 350.893561       760  .461702054   {txt}Root MSE        =   {res}  .6754

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}              credibility{col 27}{c |}      Coef.{col 39}   Std. Err.{col 51}      t{col 59}   P>|t|{col 67}     [95% Con{col 80}f. Interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 16}condition {c |}
{space 8}Japan/Commitment  {c |}{col 27}{res}{space 2}-.1666272{col 39}{space 2}  .059252{col 50}{space 1}   -2.81{col 59}{space 3}0.005{col 67}{space 4} -.282945{col 80}{space 3}-.0503094
{txt}{space 8}1.Knowledge_Japan {c |}{col 27}{res}{space 2} .0470573{col 39}{space 2} .0751486{col 50}{space 1}    0.63{col 59}{space 3}0.531{col 67}{space 4}-.1004672{col 80}{space 3} .1945818
{txt}{space 25} {c |}
condition#Knowledge_Japan {c |}
{space 6}Japan/Commitment#1  {c |}{col 27}{res}{space 2}-.0008211{col 39}{space 2}  .105321{col 50}{space 1}   -0.01{col 59}{space 3}0.994{col 67}{space 4}-.2075771{col 80}{space 3} .2059349
{txt}{space 25} {c |}
{space 20}_cons {c |}{col 27}{res}{space 2} 3.056391{col 39}{space 2} .0414113{col 50}{space 1}   73.81{col 59}{space 3}0.000{col 67}{space 4} 2.975096{col 80}{space 3} 3.137686
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regress credibility i.condition if Knowledge_Japan==0 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       520
{txt}{hline 13}{c +}{hline 34}   F(1, 518)       = {res}     8.30
{txt}       Model {c |} {res} 3.60747883         1  3.60747883   {txt}Prob > F        ={res}    0.0041
{txt}    Residual {c |} {res} 225.067521       518  .434493284   {txt}R-squared       ={res}    0.0158
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0139
{txt}       Total {c |} {res}    228.675       519  .440606936   {txt}Root MSE        =   {res} .65916

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.1666272{col 31}{space 2} .0578276{col 42}{space 1}   -2.88{col 51}{space 3}0.004{col 59}{space 4}-.2802327{col 72}{space 3}-.0530216
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.056391{col 31}{space 2} .0404158{col 42}{space 1}   75.62{col 51}{space 3}0.000{col 59}{space 4} 2.976992{col 72}{space 3}  3.13579
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store NotKnowledgable
{txt}
{com}. 
. regress credibility i.condition if Knowledge_Japan==1 & (condition==3 | condition==4)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       241
{txt}{hline 13}{c +}{hline 34}   F(1, 239)       = {res}     3.35
{txt}       Model {c |} {res} 1.68698927         1  1.68698927   {txt}Prob > F        ={res}    0.0683
{txt}    Residual {c |} {res} 120.246621       239  .503123936   {txt}R-squared       ={res}    0.0138
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0097
{txt}       Total {c |} {res}  121.93361       240  .508056708   {txt}Root MSE        =   {res} .70931

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      credibility{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}condition {c |}
Japan/Commitment  {c |}{col 19}{res}{space 2}-.1674483{col 31}{space 2} .0914454{col 42}{space 1}   -1.83{col 51}{space 3}0.068{col 59}{space 4}-.3475902{col 72}{space 3} .0126937
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.103448{col 31}{space 2}  .065858{col 42}{space 1}   47.12{col 51}{space 3}0.000{col 59}{space 4} 2.973712{col 72}{space 3} 3.233185
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store Knowledgable
{txt}
{com}. 
. coefplot NotKnowledgable Knowledgable, keep(*.condition) drop(_cons) xline(0) base title(Henerogenous Effect (Knowledge (Japan)), size(medium))
{res}{txt}
{com}. graph export "japan_knowledge.pdf", replace
{txt}(file japan_knowledge.pdf written in PDF format)

{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\L03547535\Dropbox\Tec de Monterrey\US Experiment (Prolific)\US Experiment (UNT)\JEPS Replication Files\JEPS_log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 3 May 2025, 04:31:04
{txt}{.-}
{smcl}
{txt}{sf}{ul off}